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\textbf{\large 1 The Bootstrap} \vspace{0.7cm}\newline
\textbf{\large 1.1 The Bootstrap Concept} \vspace{0.5cm}\newline
To start with we shall just consider samples \textbf{$Y$ }$=(Y_{1},$ $%
Y_{2},...,$ $Y_{n})$ and statistics $s(\mathbf{Y})$ calculated from them.
The $Y_{i}$ may themselves be vectors, and they are not necessarily
independent, though often they will be. We shall assume initially however
that the $Y_{i}$ are mutually independent so that \textbf{$Y$ }is a random
sample. (The case of several samples is considered in Section (\ref%
{Comparisons}) and the case when the $Y_{i}$ are dependent will be discussed
in Section (\ref{Timeseries}).)\vspace{0.3cm}\newline
Bootstrapping is simply a numerical way of answering the question: `What is $%
G(.),$ the probability distribution of a statistic $s($\textbf{$Y$}$),$
calculated from a sample \textbf{$Y$}$=(Y_{1},$ $Y_{2},...,$ $Y_{n})$?'%
\vspace{0.3cm}\newline
If we knew $F(.),$ the distribution of $Y$, the above question, `What is $%
G(.)$?'$,$ is easily answered numerically. We simply generate a number, $B,$
of independent samples of \textbf{$Y$}$:$ $\mathbf{y}_{1},$ $\mathbf{y}_{2},$
$...$ $\mathbf{y}_{B},$ and calculate the statistic $s_{j}=s(\mathbf{y}_{j})$
from each sample. Then the \emph{empirical distribution function} (EDF) of
the sample $\mathbf{s}=(s_{1},s_{2},...,s_{B})$, 
\begin{equation*}
G_{n}(s\text{ }|\text{ }\mathbf{s})=\frac{\#\text{ of }s_{j}\leq s}{B},
\end{equation*}%
will converge pointwise with probability one to $G(s).$ [Here and in what
follows we shall use the notation $G_{n}(.)$ to denote the EDF\ of a sample
of size $n$ drawn from the distribution $G(.).$ Where the precise sample $%
\mathbf{s}$\ used to construct $G_{n}$ needs to be made explicit then we use
the notation $G_{n}(.|$ $\mathbf{s})$ or $G_{n}(s|$ $\mathbf{s})$] The basic
sampling process is thus: \newpage \textbf{The Basic Sampling Process}%
\vspace{0.2cm}\newline
For $j=1$ to $B$ \vspace{0cm}\newline
\_For $i=1$ to $n$ \vspace{0cm}\newline
\_\_Draw $y_{ij}$ from $F(.)$ \vspace{0cm}\newline
\_Next $i$ \vspace{0cm}\newline
\_Calculate $s_{j}=s(\mathbf{y}_{j})$ \vspace{0cm}\newline
Next $j$ \vspace{0cm}\newline
Form $G_{n}(.|$ $\mathbf{s})$

\noindent The problem is that we do not know $F(.)$ usually. However we do
have the EDF, $F_{n}(.|$ $\mathbf{y}),$ formed from the sample $\mathbf{y}.$
The bootstrap idea is to use $F_{n}(.|$ $\mathbf{y})$ instead of $F(.)$ in
the above basic process. To obtain a sample we draw values, not from $F(.),$
but from $F_{n}(.|$ $\mathbf{y}).$ This is equivalent to drawing a sample of
the same size $n,$ \emph{with replacement,} from the original set of $y$'s.
We call such a sample a \emph{bootstrap sample }to distinguish it from the
original sample, and write it as $\mathbf{y}^{\ast }=(y_{1}^{\ast },$ $%
y_{2}^{\ast },$ $...,y_{n}^{\ast }).$ As in the basic process, $B$ such
bootstrap samples are drawn, and the statistic is calculated from these each
of these samples: $s_{j}^{\ast }=s(\mathbf{y}_{j}^{\ast })$. The EDF, $%
G_{n}(.$ $|$ $\mathbf{s}^{\ast }),$ of these bootstrap statistics $%
s_{j}^{\ast },$ is our estimate of $G(.).$ The bootstrap process is as
follows\vspace{0.2cm}\newline
\textbf{The Bootstrap Sampling Process}\vspace{0.2cm}\newline
Given a random sample $\mathbf{y}=(y_{1},y_{2},...y_{n})$ from $F(.)$\vspace{%
0.2cm}\newline
Form the EDF $F_{n}(.|\mathbf{y})$\vspace{0.2cm}\newline
For $j=1$ to $B$\vspace{0.2cm}\newline
\_For $i=1$ to $n$\vspace{0.2cm}\newline
\_\_Draw $y_{ij}^{\ast }$ from $F_{n}(.|\mathbf{y})$\vspace{0.2cm}\newline
\_Next $i$\vspace{0.2cm}\newline
\_Calculate $s_{j}^{\ast }=s(\mathbf{y}_{j}^{\ast })$\vspace{0.2cm}\newline
Next $j$\vspace{0.2cm}\newline
Form $G_{n}(.|\mathbf{s}^{\ast })$\vspace{0.2cm}\newline
\noindent The underlying hope and expectation is that the bootstrap process
will under many conditions reproduce the behaviour of the original process.

\pagebreak

\textbf{\large 1.2 Basic Method} \vspace{0.5cm}\newline
We shall assume that the objective is to use the random sample $\mathbf{y=}$ 
$\{y_{1},$ $y_{2},$ $...,y_{n}\}$ to estimate a parameter $\eta =\eta (F(.))$
that describes some quantity of interest of the distribution of $Y.$ Typical
examples are the mean 
\begin{equation*}
\eta =\int_{-\infty }^{\infty }ydF(y)
\end{equation*}%
or some other moment of $Y,$ or a quantile $\eta =q(p),$ defined by 
\begin{equation*}
\int_{-\infty }^{q(p)}dF(y)=p.
\end{equation*}%
The statistic $s($\textbf{$Y$}$)$ can then be regarded as any appropriate
quantity that we may care to use for estimating $\eta $. We shall emphasise
this view by using the alternative but entirely equivalent notation $\hat{%
\eta}($\textbf{$Y$}$)\equiv s($\textbf{$Y$}$),$ with the `hat' indicating an
extimated quantity. Thus, when $\eta $ is the mean, an obvious statistic is
the sample mean 
\begin{equation*}
s(\mathbf{Y})=\bar{Y}.
\end{equation*}%
It is important to realise that we do not have to use the sample version as
the estimator, assuming we can calculate it at all. In the present example
we might use a trimmed sample mean, or even the median rather than the
sample mean. What we \emph{shall} do is regard the sample version of the
parameter as being the quantity of interest in the bootstrap process, i.e.
the parameter of interest in the bootstrap process is 
\begin{equation*}
\eta ^{\ast }=\eta (F_{n}(.))
\end{equation*}%
Thus we think of the bootstrap process as one where we are generating
bootstrap samples $\mathbf{y}_{j}^{\ast }$ from which bootstrap estimates $%
s_{j}^{\ast }=s(\mathbf{y}_{j}^{\ast })$ are obtained that estimate the
bootstrap parameter $\eta ^{\ast }.$ With this viewpoint, the bootstrap
process is a prime example of the so-called \emph{plug-in approach},\emph{\ }%
being a precise analogue of the original process with the only difference
being that the known $F_{n}(.)$ is plugged-in for, i.e. replaces, the
unknown $F(.)$. The \emph{bootstrap principle }is that the bootstrap process
reproduces the properties of the original basic process. Useful quantities
that are obtained from the bootstrap process are the sample mean and
variance of the bootstrap estimates: 
\begin{equation*}
\bar{s}^{\ast }=\frac{1}{B}\sum_{j=1}^{B}s_{j}^{\ast },
\end{equation*}%
\begin{equation*}
\sigma ^{\ast 2}=\frac{1}{B-1}\sum_{j=1}^{B}(s_{j}^{\ast }-\bar{s}^{\ast
})^{2}.
\end{equation*}%
We also have an estimate of the bias of the mean of the bootstrap estimates: 
$b^{\ast }=\bar{s}^{\ast }-\eta ^{\ast }.$ A \emph{bootstrap bias adjusted
estimate} for $\eta $ is thus $\breve{\eta}=\hat{\eta}-b^{\ast }.$If the
bootstrap expectation satisfies $E^{\ast }(s_{j}^{\ast })=\hat{\eta}$ (here $%
E^{\ast }$ denotes the conditional expectation $E[s_{j}^{\ast }|$ $F_{n}(.|%
\mathbf{y})],$ where $F_{n}(.)$ is treated as being the parent
distribution), then $\bar{s}^{\ast }\rightarrow \hat{\eta}$ in probability
as $B\rightarrow \infty ,$ and we have 
\begin{equation*}
\breve{\eta}=\hat{\eta}-b^{\ast }=\hat{\eta}-(\bar{s}^{\ast }-\eta ^{\ast
})\rightarrow \hat{\eta}-(\hat{\eta}-\eta ^{\ast })=\eta ^{\ast }.
\end{equation*}%
Thus, in this case, adjusting the original estimator for bias using the
bootstrap bias is equivalent to using the bootstrap parameter $\eta ^{\ast }$
as the initial estimate\pagebreak 

\textbf{\large 1.3 The Double Bootstrap \& Bias Correction} \vspace{0.5cm}%
\newline
The bootstrap bias adjustment might not remove all the bias. At the expense
of significantly increased computation, a second application of the
bootstrap can be made. This is known as \emph{the double bootstrap}, and
consists of an outer bootstrap that is exactly the structure of the initial
bootstrap, but where each step within this outer bootstrap is itself a
(inner) bootstrap. The precise calculation is as follows.\vspace{0.2cm}%
\newline
\textbf{The Double Bootstrap (for Bias Correction)}\vspace{0.2cm}\newline
Given a random sample $\mathbf{y}=(y_{1},y_{2},...y_{n})$ from $F(.)$\vspace{%
0.2cm}\newline
\textbf{Outer Bootstrap:}\vspace{0.2cm}\newline
For $j=1$ to $B$ \vspace{0.2cm}\newline
\_For $i=1$ to $n$ \vspace{0.2cm}\newline
\_\_Draw $y_{ij}^{\ast }$ from $F_{n}(.|\mathbf{y})$ \vspace{0.2cm}\newline
\_Next $i$ \vspace{0.2cm}\newline
\_Let $\eta _{j}^{\ast \ast }=\eta (F_{n}(.|\mathbf{y}_{j}^{\ast }))$\vspace{%
0.2cm}\newline
\_\textbf{Inner Bootstrap:}\vspace{0.2cm}\newline
\_For $k=1$ to $B$ \vspace{0.2cm}\newline
\_\_For $i=1$ to $n$ \vspace{0.2cm}\newline
\_\_\_Draw $y_{ijk}^{\ast \ast }$ from $F_{n}(.|\mathbf{y}_{j}^{\ast })$ 
\vspace{0.2cm}\newline
\_\_Next $i$ \vspace{0.2cm}\newline
\_\_Calculate $s_{jk}^{\ast \ast }=s(\mathbf{y}_{jk}^{\ast \ast })$ \vspace{%
0.2cm}\newline
\_Next $k$ \vspace{0.2cm}\newline
\_Let $\bar{s}_{j\cdot }^{\ast \ast }=\frac{1}{B}\sum_{k=1}^{B}s_{jk}^{\ast
\ast }$ \vspace{0.2cm}\newline
\_Let $b_{j}^{\ast \ast }=\bar{s}_{j\cdot }^{\ast \ast }-\eta _{j}^{\ast
\ast }$ \vspace{0.2cm}\newline
\_Let $\breve{\eta}_{j}^{\ast }=\hat{\eta}_{j}^{\ast }-b_{j}^{\ast \ast }$ 
\vspace{0.2cm}\newline
\textbf{\_End of Inner Bootstrap} \vspace{0.2cm}\newline
Next $j$ \vspace{0.2cm}\newline
Let $\bar{\breve{\eta}}_{\cdot }^{\ast }=\frac{1}{B}\sum_{j=1}^{B}\breve{\eta%
}_{j}^{\ast }$ \vspace{0.2cm}\newline
Let $b^{\ast }=\bar{\breve{\eta}}_{\cdot }^{\ast }-\eta ^{\ast }$ \vspace{%
0.2cm}\newline
Let $\breve{\eta}=\hat{\eta}-b^{\ast }$ \vspace{0.2cm}\newline
\textbf{End of Outer Bootstrap} \vspace{0.2cm}\newline
\noindent Whilst this can lead to a reduction in the bias, the effect on the
variance of the estimator is not so clear. The main obvious penalty is the
increase in computing cost. If a full numerical method is used to carry out
the double bootstrap then the computation increases quadratically with $B.$
The full double bootstrap is not always necessary. It may be possible to
avoid the inner bootstrap by using an estimator of $\eta $ that is already
bias adjusted. For example if it is thought that the original estimator
might seriously biased then one might replace it immediately with $\eta
^{\ast }$ and then use bootstrap bias adjustment on $\eta ^{\ast }$ using
the single bootstrap. The single bootstrap yields an estimate of the
variance of $\hat{\eta}^{\ast }.$ When the bias correction is small this
same variance will of course estimate the variance of $\breve{\eta}$ as
well. However when the bias correction is large then the double bootstrap
has to be used\pagebreak 

\textbf{{\large 1.4 \label{Parametric}Parametric Bootstrap}} \vspace{0.5cm}%
\newline
We now consider parametric bootstrapping. Again we focus on a random sample $%
\mathbf{y}=(y_{1},$ $y_{2},$ $...,$ $y_{n})$ drawn from a distribution $F(y,$
$\mathbf{\theta }),$ with the difference that the form of $F$ is known but
which depends on a vector $\mathbf{\theta }$ of parameters that is unknown.
We denote the unknown true value of $\mathbf{\theta }$ by $\mathbf{\theta }%
_{0}.$ It is then natural to carry out bootstrap sampling, not from the EDF $%
F_{n}(y$ $|$ $\mathbf{y}),$ but from the distribution $F(y,$ $\mathbf{\hat{%
\theta}}),$ where $\mathbf{\hat{\theta}}$ is an estimate of $\mathbf{\theta }%
_{0}.$ An obvious choice for $\mathbf{\hat{\theta}}$ is the \emph{maximum
likelihood} (ML) estimator because it has an asymptotic distribution that is
easy to calculate. We define the log-likelihood by 
\begin{equation}
L(\mathbf{\theta },\text{ }\mathbf{y})=\sum_{i=1}^{n}\log f(y_{i};\mathbf{%
\theta }).  \label{1}
\end{equation}%
where $\mathbf{y}=(y_{1},$ $y_{2},$ $...,$ $y_{n}).$ The method of ML is to
estimate $\mathbf{\theta }_{0}$ by maximizing $L$, treated as a function of $%
\mathbf{\theta ,}$ subject to $\mathbf{\theta }\in \mathbf{\Theta ,}$ where $%
\mathbf{\Theta }$ is some allowed region of the parameter space. for the
remainder of the subsection\textbf{\ }$\mathbf{\hat{\theta}}$ will represent
the ML estimator. Regularity conditions which ensure that standard
asymptotic theory applies are given in Wilks (1962) or Cox and Hinkley
(1974) for example. When they apply $\mathbf{\hat{\theta}}$ can found by
solving $\left. \frac{\partial L(\mathbf{\theta })}{\partial \mathbf{\theta }%
}\right\vert _{\mathbf{\hat{\theta}}}=\mathbf{0}$. Moreover 
\begin{equation}
-n^{-1}\left. \frac{\partial ^{2}L(\mathbf{\theta })}{\partial \mathbf{%
\theta }^{2}}\right\vert _{\mathbf{\theta }_{0}}\rightarrow I(\mathbf{\theta 
}_{0}),  \label{lik2}
\end{equation}%
\noindent a positive definite matrix, and the asymptotic distribution of $%
\mathbf{\hat{\theta},}$ as $n\rightarrow \infty $, is%
\begin{equation}
n^{1/2}(\mathbf{\hat{\theta}}-\mathbf{\theta }_{0})\text{ is }N(\mathbf{0},V(%
\mathbf{\theta }_{0}))  \label{Distnoftheta}
\end{equation}%
where $V(\mathbf{\theta }_{0})=[I(\mathbf{\theta }_{0})]^{-1}.$ From (\ref%
{lik2}) a reasonable approximation for $I(\mathbf{\theta }_{0})$ is 
\begin{equation}
I(\mathbf{\hat{\theta}})\simeq -n^{-1}\left. \frac{\partial ^{2}L(\mathbf{%
\theta })}{\partial \mathbf{\theta }^{2}}\right\vert _{\mathbf{\hat{\theta}}%
}.  \label{Ihat}
\end{equation}%
More generally suppose that $\eta $ is a smooth function of $\mathbf{\theta }
$, ie $\eta =\eta (\mathbf{\theta }).$ The ML estimate of $\eta $ is then
simply 
\begin{equation*}
\hat{\eta}=\eta (\mathbf{\hat{\theta}}),
\end{equation*}%
where $\mathbf{\hat{\theta}}$ is the ML estimate of $\mathbf{\theta }$. The
asymptotic normality of $\hat{\eta}$ follows from that of $\mathbf{\hat{%
\theta}}$, assuming that $\eta (\mathbf{\theta })$ can be approximated by a
linear Taylor series in $\mathbf{\theta }.$ This method of obtaining the
asymptotic behaviour of $\hat{\eta}$ is called the \emph{delta method}. We
have 
\begin{equation}
n^{1/2}(\hat{\eta}-\eta _{0})\sim N(0,\sigma ^{2}(\mathbf{\theta }_{0}))
\label{etahatdist}
\end{equation}%
with 
\begin{equation}
\sigma ^{2}(\mathbf{\theta }_{0})=\mathbf{g}^{T}(\mathbf{\theta }_{0})V(%
\mathbf{\theta }_{0})\mathbf{g}(\mathbf{\theta }_{0})  \label{sigsq}
\end{equation}%
where 
\begin{equation}
\mathbf{g}(\mathbf{\theta }_{0})=\left. \frac{\partial \eta (\mathbf{\theta }%
)}{\partial \mathbf{\theta }}\right\vert _{\mathbf{\theta }_{0}}.
\label{gradeta}
\end{equation}%
An approximation for the distribution of $\hat{\eta}$ is given by (\ref%
{etahatdist}) with $\mathbf{\hat{\theta}}$ used in place of the unknown $%
\mathbf{\theta }_{0}.$ The above asymptotic theory is firmly established and
much used in practice. For this reason parametric bootstrapping is not
especially needed when parametric models are fitted to large samples.
However, for small samples Monte-Carlo simulation is a well known method for
examining the behaviour of estimators and fitted distributions in parametric
problems . Such methods are actually parametric bootstrapping under a
different name and as such have been known and used for a long time. In this
sense parametric bootstrapping significantly predates the direct
non-parametric bootstrap. Parametric bootstrapping lacks the computational
novelty of direct sampling in that resampling is not involved. Perhaps it is
because of this that the significance of parametric bootstrapping was not
fully appreciated, and its theoretical importance not recognized until the
landmark paper of Efron (1979). If we view bootstrapping as a numerical
sampling approach involving models that may include parameters, then its
potential is significantly extended.\pagebreak 

{\large \noindent }\textbf{\large 2 Percentiles and Confidence Intervals} 
\vspace{0.5cm}\newline
\textbf{\large 2.1 Percentiles} \vspace{0.5cm}\newline
We shall denote the estimate of a percentile $q_{p}$, with percentile
probability $p,$ by $\hat{q}_{p}(\mathbf{y}).$ The obvious choice for
estimating a percentile non-parametrically is to use the corresponding
percentile of the EDF. There is a certain ambiguity in doing this as the EDF
is a step function. Thus all percentiles in the interval $(\frac{k-1}{n},%
\frac{k}{n})$ are estimated by the observed order statistic $y_{(k)}.$
Conversely the percentile $\frac{k}{n}$ is estimated by any point $y$ for
which $F_{n}(y)=k/n.$ Such ambiguities are removed if the EDF is smoothed.
One way is to use a kernel estimator of the density but in most simulations
this is perhaps unnecessarily elaborate. The simplest smoothing procedure is
to note that if $Y_{(k)}$ is the $k$th order statistic (as opposed to the
observed value $y_{(k)}$) then $E[F(Y_{(k)})]=k/(n+1)$ and to use this value
to estimate the value of $F$ at the observed order statistic $y_{(k)}.$ We
can now simply interpolate between these estimated points $(y_{(k)},$ $%
k/(n+1))$ for $k=1,2,...,n.$ The range can be extended to $(0,0)$ (using the
line segment joining $(0,0)$ and $(y_{(1)},$ $1/(n+1))$ if $Y$ is known to
be a positive random variable. If we denote this smoothed version of $%
F_{n}(.)$ by $\tilde{F}_{n}(.)$ then an obvious estimator for $\hat{q}_{p}(%
\mathbf{y})$ is 
\begin{equation*}
\hat{q}_{p}(\mathbf{y})=\tilde{F}_{n}^{-1}(p)
\end{equation*}%
Estimating $F(.)$ in the range $(y_{(n)},\infty ),$ or equivalently $q_{p}$
for $p>n/(n+1)$ is not advisable unless the tail behaviour of $F$ is known.
The bootstrap analogue of $\hat{q}_{p}(\mathbf{y})$ is obtained in the usual
way. We draw $\mathbf{y}^{\ast }$, a bootstrap sample from $\mathbf{y,}$ and
construct $\tilde{F}_{n}(.|$ $\mathbf{y}^{\ast }),$ the smoothed version of
the bootstrap EDF. [Where there is no ambiguity we shall write $F_{n}^{\ast
}(.)$ for $F_{n}(.|$ $\mathbf{y}^{\ast })$ and $\tilde{F}_{n}^{\ast }(.)$
for $\tilde{F}_{n}(.|$ $\mathbf{y}^{\ast }).$] We can now calculate $\hat{q}%
_{p}^{\ast }$ as $\hat{q}_{p}^{\ast }=\tilde{F}_{n}^{\ast -1}(p).$ The
bootstrap parameter here is $\eta ^{\ast }=\hat{q}_{p}(\mathbf{y}).$ In the
parametric case, things are much easier. Quantiles are simply estimated from
the fitted distribution $F(.,\mathbf{\hat{\theta}})$\pagebreak 

\textbf{\large 2.2 Confidence Intervals by Direct Bootstrapping} \vspace{%
0.5cm}\newline
We now consider the construction of confidence intervals for a parameter of
interest, $\eta ,$ and consider to what extent the corresponding bootstrap
process. can be used to supply a confidence interval for this original
parameter $\eta .$ The key requirement is that the distribution of the
bootstrap difference $\hat{\eta}^{\ast }-\eta ^{\ast }$ should be close to
that of $\hat{\eta}-\eta .$ Roughly speaking we need 
\begin{equation}
P^{\ast }(\sqrt{n}(\hat{\eta}^{\ast }-\eta ^{\ast })\leq y)-P(\sqrt{n}(\hat{%
\eta}-\eta )\leq y)\rightarrow 0  \label{p*-p}
\end{equation}%
for any $y$ as $n\rightarrow \infty .$ (As before with $E^{\ast }$, $P^{\ast
}$ denotes the conditional probability evaluation treating $F_{n}(.|$ $%
\mathbf{y})$ as the parent distribution.) More precise statements of this
convergence (\ref{p*-p}) and other results are given in Section (\ref{Theory}%
). We can proceed as follows. We generate $B$ bootstrap samples $\mathbf{y}%
_{j}^{\ast },$ $j=1,2,...,B$ and corresponding bootstrap estimates $\hat{\eta%
}_{j}^{\ast },$ $j=1,2,...,B.$ Then we select an appropriate confidence
level $(1-2\alpha )$ (we use $2\alpha $ rather than $\alpha $ so that $%
\alpha $ corresponds to each of the two tail probabilities in two-sided
intervals) and find values $a^{\ast }$ and $b^{\ast }$ for which 
\begin{equation*}
P^{\ast }(a^{\ast }\leq \hat{\eta}^{\ast }-\eta ^{\ast }\leq b^{\ast
})\simeq 1-2\alpha .
\end{equation*}%
We then appeal to (\ref{p*-p}) to replace $\hat{\eta}^{\ast }-\eta ^{\ast }$
by $\hat{\eta}-\eta $ and obtain 
\begin{equation*}
P(a^{\ast }\leq \hat{\eta}-\eta \leq b^{\ast })\simeq 1-2\alpha 
\end{equation*}%
which on inversion gives the approximate $(1-2\alpha )$ confidence interval 
\begin{equation}
\hat{\eta}-b^{\ast }\leq \eta \leq \hat{\eta}-a^{\ast }  \label{basicCI}
\end{equation}%
for $\eta .$ One way of obtaining $a^{\ast }$ and $b^{\ast }$ is as follows.
Let the bootstrap estimates of the $\alpha $ and $(1-\alpha )$ quantiles
obtained from the smoothed EDF$\ $of the $\hat{\eta}_{j}^{\ast }$'s be $\hat{%
q}_{\alpha }(\mathbf{\hat{\eta}}^{\ast })$ and $\hat{q}_{1-\alpha }(\mathbf{%
\hat{\eta}}^{\ast }).$ These are estimates of the quantiles of the
distribution of $\hat{\eta}^{\ast };$ so the corresponding quantiles of $%
\hat{\eta}^{\ast }-\eta ^{\ast }$ are 
\begin{equation}
a^{\ast }=\hat{q}_{\alpha }(\mathbf{\hat{\eta}}^{\ast })-\eta ^{\ast }\text{
and }b^{\ast }=\hat{q}_{1-\alpha }(\mathbf{\hat{\eta}}^{\ast })-\eta ^{\ast
}.  \label{a*b*}
\end{equation}%
Substituting these into (\ref{basicCI}) and noting that $\eta ^{\ast }=\hat{%
\eta}$ gives what is normally called \emph{the basic bootstrap confidence
interval.} 
\begin{subequations}
\begin{equation}
2\hat{\eta}-\hat{q}_{1-\alpha }(\mathbf{\hat{\eta}}^{\ast })\leq \eta \leq 2%
\hat{\eta}-\hat{q}_{\alpha }(\mathbf{\hat{\eta}}^{\ast }).  \label{basicBSCI}
\end{equation}%
This confidence interval is also known as the \emph{hybrid bootstrap
confidence interval} as we have essentially replaced percentage points of
the unknown original EDF by those of the smoothed bootstrap EDF. There is
substantial evidence that this basic bootstrap can be significantly improved
(in terms of giving coverage that is closer to the nominal level) if we use
a \emph{pivotal} quantity, like a studentized mean, rather than focusing
just on the difference $\hat{\eta}-\eta $. The reason is that a pivotal
quantity is less dependent (ideally not dependent at all) ) on the form of
the original distribution. This would mean that the confidence interval is
less influenced by any difference there may be in the distributions of $\hat{%
\eta}^{\ast }-\eta ^{\ast }$ and $\hat{\eta}-\eta .$ We consider this
next.\newpage \textbf{\large 2.3 Studentization} \vspace{0.5cm}\newline
Suppose that $\hat{\sigma}^{2}(\mathbf{y}),$ an estimate of the variance of $%
\hat{\eta},$ can be calculated from the sample $\mathbf{y}$. This will be
the case for $\hat{\eta}=\bar{y}$ say, when we have $\hat{\sigma}^{2}(%
\mathbf{y})=s^{2},$ the sample variance. As before suppose we can find $%
a^{\ast }$ and $b^{\ast }$ such that 
\end{subequations}
\begin{equation*}
P^{\ast }(a^{\ast }\leq (\hat{\eta}^{\ast }-\eta ^{\ast })/\hat{\sigma}%
^{\ast }\leq b^{\ast })\simeq 1-2\alpha .
\end{equation*}%
If we then appeal to a similar result to (\ref{p*-p}) and replace $(\hat{\eta%
}^{\ast }-\eta ^{\ast })/\hat{\sigma}^{\ast }$ by $(\hat{\eta}-\eta )/\hat{%
\sigma},$ we obtain 
\begin{equation*}
P(\hat{\sigma}a^{\ast }\leq \hat{\eta}-\eta \leq \hat{\sigma}b^{\ast
})\simeq 1-2\alpha 
\end{equation*}%
which on inversion gives the approximate $(1-2\alpha )$ confidence interval 
\begin{equation*}
\hat{\eta}-\hat{\sigma}b^{\ast }\leq \eta \leq \hat{\eta}-\hat{\sigma}%
a^{\ast }.
\end{equation*}%
We can now calculate $a^{\ast }$ and $b^{\ast }$ by drawing $B$ bootstrap
versions of $z=$ $(\hat{\eta}-\eta )/\hat{\sigma}:$ $z_{j}^{\ast }=(\hat{\eta%
}_{j}^{\ast }-\eta ^{\ast })/\hat{\sigma}_{j}^{\ast },$ $j=1,2,...,B.$ Let $%
\hat{q}_{\alpha }(\mathbf{z}^{\ast })$ and $\hat{q}_{1-\alpha }(\mathbf{z}%
^{\ast })$ be the quantiles obtained from the EDF of these $z_{j}^{\ast }.$
The confidence interval for $\eta $ is now 
\begin{equation}
(\hat{\eta}_{\alpha }^{Stud},\text{ }\hat{\eta}_{1-\alpha }^{Stud})=(\hat{%
\eta}-\hat{\sigma}\hat{q}_{1-\alpha }(\mathbf{z}^{\ast }),\text{ }\hat{\eta}-%
\hat{\sigma}\hat{q}_{\alpha }(\mathbf{z}^{\ast })).  \label{studBSCI}
\end{equation}%
This is usually called the \emph{studentized bootstrap confidence interval}.
Studentized bootstrap intervals can be readily used with the parametric
model $F(y,\mathbf{\theta })$ of Section (\ref{Parametric}). Suppose that $%
\mathbf{y}$ is a random sample of size $n$ drawn from the distribution $F(y,%
\mathbf{\theta }_{0}),$ and that $\eta _{0}=\eta (\mathbf{\theta }_{0})$ is
the quantity of interest. We can estimate $\eta _{0}$ using $\hat{\eta}=\eta
(\mathbf{\hat{\theta}})$ where $\mathbf{\hat{\theta}}$ is the MLE of $%
\mathbf{\theta }_{0}.$When $n$ is large we can use the asymptotic
approximation 
\begin{equation*}
n^{1/2}(\hat{\eta}-\eta _{0})/\sigma (\mathbf{\hat{\theta}})\sim N(0,1).
\end{equation*}%
When $n$ is not large it is worth employing bootstrapping. We draw $B$
bootstrap samples $\mathbf{y}_{j}^{\ast }$ $j=1,2,...,B$ from the fitted
distribution $F(y,\mathbf{\hat{\theta}}).$ From each sample we obtain the
bootstrap ML estimator $\mathbf{\hat{\theta}}_{j}^{\ast }$ and bootstrap
studentized quantity $z_{j}^{\ast }=n^{1/2}(\hat{\eta}_{j}^{\ast }-\eta
^{\ast })/\sigma (\mathbf{\hat{\theta}}_{j}^{\ast }).$ The quantiles $\hat{q}%
_{\alpha }(\mathbf{z}^{\ast })$ and $\hat{q}_{1-\alpha }(\mathbf{z}^{\ast })$
can be obtained from the EDF of these $z_{j}^{\ast }$ and the studentized
interval (\ref{studBSCI}) constructed. In the non-parametric case an
estimate of the variance of $\hat{\eta}$ may not be immediately available.
One possibility is to use the double bootstrap as given previously (where $s=%
\hat{\eta})$ with the inner loop taking the form \vspace{0.2cm}\newline
\_\textbf{Inner Bootstrap:} \vspace{0.2cm}\newline
\_For $k=1$ to $B$ \vspace{0.2cm}\newline
\_\_For $i=1$ to $n$ \vspace{0.2cm}\newline
\_\_\_Draw $y_{ijk}^{\ast \ast }$ from $F_{n}(.|\mathbf{y}_{j}^{\ast })$ 
\vspace{0.2cm}\newline
\_\_Next $i$ \vspace{0.2cm}\newline
\_\_Let $s_{jk}^{\ast \ast }=s(\mathbf{y}_{jk}^{\ast \ast })$ \vspace{0.2cm}%
\newline
\_Next $k$ \vspace{0.2cm}\newline
\_Let $\hat{\eta}_{j}^{\ast }=s(\mathbf{y}_{j}^{\ast })$ \vspace{0.2cm}%
\newline
\_Let $\bar{s}_{j\cdot }^{\ast \ast }=\frac{1}{B}\sum_{k=1}^{B}s_{jk}^{\ast
\ast }$ \vspace{0.2cm}\newline
\_Let $v_{j}^{\ast \ast }=\frac{1}{B-1}\sum_{k=1}^{B}(s_{jk}^{\ast \ast }-%
\bar{s}_{j\cdot }^{\ast \ast })^{2}$ \vspace{0.2cm}\newline
\_Let $z_{j}^{\ast }=n^{1/2}(\hat{\eta}_{j}^{\ast }-\eta ^{\ast
})/(v_{j}^{\ast \ast })^{1/2}$ \vspace{0.2cm}\newline
\textbf{\_End of Inner Loop} \vspace{0.2cm}\newline
\noindent If the double bootstrap is considered computationally too
expensive, then an alternative using \emph{influence functions }can be used
provided our statistic is expressible as functional of the EDF, i.e. $s(%
\mathbf{y})=\eta \lbrack F_{n}(.|\mathbf{y})].$ Such statistics are termed 
\emph{statistical functions, }and were introduced by von Mises (1947). We
then assume that the relevant functional $\eta =\eta \lbrack F(.)]$ has a
linearised form (akin to a first order linear generalisation of a Taylor
series): $\eta (G(.))$ $\simeq \eta (F(.))$ $+$ $\int L_{\eta }(y|F(.))dG(y)$%
. Here 
\begin{eqnarray}
L_{\eta }(y|F(.)) &=&\frac{\partial \eta \{(1-\varepsilon )F(.)+\varepsilon
H_{y}(.)\}}{\partial \varepsilon }  \label{influencefn} \\
&=&\lim_{\varepsilon \rightarrow 0}\varepsilon ^{-1}[\eta \{(1-\varepsilon
)F(.)+\varepsilon H_{y}(.)\}  \notag \\
&&-\eta (F(.))],  \notag
\end{eqnarray}%
the derivative of $\eta $ at $F,$ is called the \emph{influence function} of 
$\eta ,$ and $H_{y}(x)$ is the Heaviside unit step function with jump from $0
$ to $1$ at $x=y.$ The sample approximation 
\begin{equation}
l(y|\mathbf{y})=L_{\eta }(y|F_{n}(.|\mathbf{y}))  \label{EmpInfluenceFn}
\end{equation}%
is called the \emph{empirical influence function}. An analogous argument to
that used in the delta method yields the non-parametric analogue of (\ref%
{sigsq}) as 
\begin{equation*}
\mathrm{Var}[\eta \{F(.)\}]=n^{-1}\int L_{\eta }^{2}\{y|F(.)\}dF(y),
\end{equation*}%
with sample version 
\begin{equation*}
\mathrm{Var}[\eta \{F_{n}(.|\mathbf{y})\}]=n^{-2}\sum_{j=1}^{n}l^{2}(y_{j}|%
\mathbf{y}),
\end{equation*}%
where the $l(y_{j}|\mathbf{y})$ are the \emph{empirical function values}
evaluated at the observed values $y_{j}.$ In practice these values have to
be evaluated numerically using (\ref{influencefn}) with, typically, $%
\varepsilon =(100n)^{-1}.$ The ease with which the empirical influence
function can be evaluated is rather problem dependent, and the potential
complexity of this calculation detracts from this approach. One simple
approximation is the jacknife approximation 
\begin{equation*}
l_{jack}(y_{j}|\mathbf{y})=(n-1)(\hat{\eta}(\mathbf{y})-\hat{\eta}(\mathbf{y}%
_{\smallsetminus j}))
\end{equation*}%
where $\hat{\eta}(\mathbf{y}_{\smallsetminus j})$ is the estimate calculated
from the sample $\mathbf{y}$ but with the observation $y_{j}$ omitted. (See
Davison and Hinkley 1997, problem 2.18.)\pagebreak 

\textbf{\large 2.4 Percentile Methods} \vspace{0.5cm}\newline
Suppose now that the distribution of $\hat{\eta}$ is symmetric, or more
generally, there is some transformation $w=h(\hat{\eta})$ for which the
distribution is symmetric. Examination of the form of the bootstrap
percentiles (\ref{a*b*}), (see for example Hjorth, 1994, Section 6.6.2)
shows that they do not depend on the explicit form of $h(.)$ at all. Instead
we find that they can, with a change of sign, be swapped, ie $a^{\ast }$ is
replaced by $-b^{\ast }$ and $b^{\ast }$ by $-a^{\ast }.$ Then (\ref{basicCI}%
) becomes 
\begin{equation}
(\hat{\eta}_{\alpha }^{Per},\text{ }\hat{\eta}_{1-\alpha }^{Per})=(\hat{q}%
_{\alpha }(\mathbf{\hat{\eta}}^{\ast }),\text{ }\hat{q}_{1-\alpha }(\mathbf{%
\hat{\eta}}^{\ast })).  \label{PercentileCI}
\end{equation}%
This $(1-2\alpha )$ confidence interval is known as the \emph{bootstrap
percentile interval}. This confidence interval is easy to implement but can
be improved. We give two variants called the \emph{bias corrected} (BC) and 
\emph{accelerated bias corrected }(BCa) \emph{percentile methods.} The basic
idea in the BCa method is that there is a studentized transformation $g$ for
which 
\begin{equation}
\frac{g(\hat{\eta})-g(\eta )}{1+ag(\eta )}+b\thicksim N(0,1).
\label{BCaAssumption}
\end{equation}%
The BC method is simply the special case where $a=0$. If we calculate a one
sided confidence interval for $\eta $ under the assumption (\ref%
{BCaAssumption}) we find that if we set 
\begin{equation*}
\beta _{1}=\Phi \left( b+\frac{b+z_{\alpha }}{1-a(b+z_{\alpha })}\right) 
\end{equation*}%
where $\Phi (.)$ is the standard normal distribution function, then with
confidence $(1-\alpha )$%
\begin{equation*}
\hat{q}_{\beta _{1}}(\mathbf{\hat{\eta}}^{\ast })<\eta 
\end{equation*}%
where $\hat{q}_{\beta }(\mathbf{\hat{\eta}}^{\ast })$ is the bootstrap
percentile of the $\mathbf{\hat{\eta}}^{\ast }$ with probability $\beta .$
In a similar way a two sided $1-2\alpha $ confidence interval is 
\begin{equation*}
\hat{q}_{\beta _{1}}(\mathbf{\hat{\eta}}^{\ast })<\eta <\hat{q}_{\beta _{2}}(%
\mathbf{\hat{\eta}}^{\ast })
\end{equation*}%
where 
\begin{equation*}
\beta _{2}=\Phi \left( b+\frac{b+z_{1-\alpha }}{1-a(b+z_{1-\alpha })}\right)
.
\end{equation*}%
The bias parameter $b$ is obtained as 
\begin{equation*}
b=\Phi ^{-1}\{\breve{F}_{n}(\hat{\eta}\text{ }|\text{ }\mathbf{\hat{\eta}}%
^{\ast })\}
\end{equation*}%
where $\tilde{F}_{n}(\hat{\eta}$ $|$ $\mathbf{\hat{\eta}}^{\ast })$ is the
smoothed EDF of the bootstrap sample $\mathbf{\hat{\eta}}^{\ast }$ evaluated
at the original estimated value $\hat{\eta}.$ If we now set $a=0$ this gives
the BC method. We denote the resulting two-sided version of the confidence
interval by $(\hat{\eta}_{\alpha }^{BC},$ $\hat{\eta}_{1-\alpha }^{BC}).$
The acceleration parameter $a$ is arguably less easily calculated. Efron
(1987) suggests the approximation (which is one-sixth the estimated standard
skewness of the linear approximation to $\eta ):$%
\begin{equation*}
a=\frac{1}{6}\frac{\sum_{i=1}^{n}l^{3}(y_{i}|\mathbf{y})}{%
[\sum_{i=1}^{n}l^{2}(y_{i}|\mathbf{y})]^{3/2}}
\end{equation*}%
where $l(y_{i}|\mathbf{y})$ are the values of the empirical influence
function (\ref{EmpInfluenceFn}) evaluated at the observations $y_{i}.$ We
denote the two-sided version of the confidence interval given by this BCa
method by $(\hat{\eta}_{\alpha }^{BCa},$ $\hat{\eta}_{1-\alpha }^{BCa}).$%
\pagebreak 

\textbf{{\large 3 \label{Theory}Theory}} \vspace{0.5cm}\newline
Like many statistical methods, understanding of the practical usefulness of
the bootstrap as well as its limitations has been built up gradually with
experience through applications. This practical experience has been
underpinned by a growing asymptotic theory which provides a basis for
understanding when bootstrapping will work and when it will not. A truly
general theory rapidly becomes very technical and is still incomplete. A
detailed treatment is not attempted here, but some of the more accessible
and useful results are summarized. Bootstrapping relies on sampling from the
EDF $F_{n}(.|\mathbf{y})$ reproducing the behaviour of sampling from the
original distribution $F(.).$ We therefore need convergence of $F_{n}(.|%
\mathbf{y})$ to $F(.).$ This is confirmed by the Glivenko-Cantelli theorem
which guarantees strong convergence, i.e. 
\begin{equation*}
\sup_{y}\left\vert F_{n}(y|\mathbf{y})-F(y)\right\vert \rightarrow 0\text{
with probability 1}
\end{equation*}%
as $n\rightarrow \infty .$ Though reassuring this does not throw any direct
light on the bootstrap process. To investigate the bootstrap process we look
instead at the equally well-known result that the process 
\begin{equation*}
Z_{n}(y)=\sqrt{n}(F_{n}(y)-F(y))
\end{equation*}%
converges in probability to the Brownian bridge $B(F(y))$ as $n\rightarrow
\infty .$ [A Brownian bridge $B(t),$ $0<t<1,$ is a Gaussian process for
which $B(0)=B(1)=0,$ $E[B(u)]=0$ and $E[B(u)B(v)]=u(1-v)$ for $0<u<v<1$.]
Bickel and Freedman (1981) give the following bootstrap version of this.%
\vspace{0.5cm}\newline
\noindent \textbf{Theorem }Let $Y_{1},$ $Y_{2},...$ be a sequence of
independent observations from $F(.),$ with $\mathbf{Y}_{n}$ comprising the
first $n$ values. Then for almost all such sequences (i.e. with probability
1) the bootstrap process 
\begin{equation*}
Z_{n}^{\ast }(y)=\sqrt{n}(F_{n}^{\ast }(y|\mathbf{Y}_{n})-F_{n}(y))
\end{equation*}%
converges in probability to the Brownian bridge $B(F(y))$ as $n\rightarrow
\infty .$ An interesting immediate application of this result to the
calculation of confidence bands for $F(y)$ is given by Bickel and Freedman.
We set a confidence level $1-\alpha $ and, from the bootstrap process, we
then select $c_{n}(\alpha )$ so that, as $n\rightarrow \infty ,$%
\begin{equation*}
P^{\ast }\{\sup_{y}\left\vert Z_{n}^{\ast }(y)\right\vert \leq c_{n}(\alpha
)\}\rightarrow 1-\alpha .
\end{equation*}%
Then, as $Z_{n}^{\ast }(y)$ and $Z_{n}(y)$ converge to the same process $%
B(F(y)),$ we have also that 
\begin{equation*}
P^{\ast }\{\sup_{y}\left\vert Z_{n}(y)\right\vert \leq c_{n}(\alpha
)\}\rightarrow 1-\alpha .
\end{equation*}%
Thus an asymptotically correct $(1-\alpha )$ confidence band for $F(y)$ is 
\begin{equation*}
F_{n}(y)\pm \frac{c_{n}(\alpha )}{\sqrt{n}}.
\end{equation*}%
We now consider the distribution of an estimator $\hat{\eta}$ of a parameter 
$\eta .$ We need the distributions of the bootstrap estimator $\hat{\eta}%
^{\ast }$ and of the difference $\hat{\eta}^{\ast }-\eta ^{\ast }$ to
reproduce respectively the distributions of $\hat{\eta}$ $(\equiv \eta
^{\ast })$ and of $\hat{\eta}-\eta .$ The most general results obtained to
date assume that the statistic can be expressed as $s=\eta (F_{n})$ where
both the EDF $F_{n}$ and the underlying distribution $F$ belong to a space $%
\mathcal{F}$ of distributions.\ Moreover such results require $s$ to be
differentiable in such a way that a linear approximation can be formed with,
as first order terms, the previously described influence functions, i.e. 
\begin{equation*}
\eta (F_{n})=\eta (F)+n^{-1}\sum_{j=1}^{n}l(y_{j}|\mathbf{y}%
)+o_{p}(n^{-1/2}).
\end{equation*}%
Then it is often possible to establish the analogous result for the
bootstrap version 
\begin{equation*}
\eta (F_{n}^{\ast })=\eta (F)+n^{-1}\sum_{j=1}^{n}l(y_{j}^{\ast }|\mathbf{y}%
^{\ast })+o_{p}(n^{-1/2}),
\end{equation*}%
and, provided $\mathrm{Var}[\eta (F)]$ is finite, then, in probability, the
sequence $y_{1},y_{2},...$ is such that 
\begin{equation*}
\sup_{s}\left\vert P^{\ast }(\eta (F_{n}^{\ast })\leq s)-P(\eta (F_{n})\leq
s)\right\vert \rightarrow 0
\end{equation*}%
as $n\rightarrow \infty .$ Here we have written $P^{\ast }(\eta (F_{n}^{\ast
})\leq s)$ for $P(\eta (F_{n}^{\ast })\leq s$ $|$ $F_{n}).$ If, in addition, 
$s$ is \emph{continuously} differentiable, then we can usually strengthen
the result to almost sure convergence. A detailed discussion is given by
Shao and Tu (1995) who give examples of $\hat{\eta}(F)$ that satisfy such
conditions. Convergence has been studied in detail for the particular case
of means and percentiles. In this case a more accessible approach using the
Berry-Ess\'{e}en inequality is possible, and this we discuss in the next
section.

\pagebreak

\textbf{\large 3.1 Convergence Rates}\vspace{0.5cm}\newline
Efron (1979) considered the finite case where $Y$ takes just a finite set of
values which we can take to be $\{1,2,...,m\}$ with probabilities $\mathbf{p=%
}(p_{1},p_{2},...,p_{m})^{T}.$ We shall not discuss this particular case, a
good summary for which is provided by Hjorth (1994). We shall instead focus
on the continuous case where $\eta $ $=\mu \equiv E(Y)$, with $\hat{\eta}=%
\bar{Y}_{n}=\sum_{i=1}^{n}Y_{i}/n$. We also write $Var(Y)=\sigma ^{2},$ $%
\rho =E(|Y|^{3}).$ The well-known Berry-Ess\'{e}en theorem (see Serfling,
1980, for example) then provides a powerful result for investigating
bootstrap convergence. To avoid long formulas we shall write: 
\begin{equation*}
H_{n}(y)=P(\sqrt{n}(\bar{Y}_{n}-\mu )\leq y)
\end{equation*}%
for the (unknown) CDF of interest and 
\begin{equation*}
H_{n}^{\ast }(y)=P^{\ast }(\sqrt{n}(\bar{Y}_{n}^{\ast }-\bar{Y}_{n})\leq y)
\end{equation*}%
for the bootstrap version of this. We write 
\begin{equation*}
\tilde{H}_{n}(y)=P(\sqrt{n}\frac{(\bar{Y}_{n}-\mu )}{\sigma }\leq y)
\end{equation*}%
for the (unknown) \emph{standardized} CDF of interest, 
\begin{equation*}
\breve{H}_{n}(y)=P(\sqrt{n}\frac{(\bar{Y}_{n}-\mu )}{\hat{\sigma}_{n}}\leq y)
\end{equation*}%
for the studentized CDF, and 
\begin{equation*}
\tilde{H}_{n}^{\ast }(y)=P^{\ast }(\sqrt{n}\frac{(\bar{Y}_{n}^{\ast }-\bar{Y}%
_{n})}{\hat{\sigma}_{n}}\leq y)
\end{equation*}%
for its bootstrap \emph{studentized} version. We write also $\Phi (y)$ for
the CDF of the standard normal distribution. The Berry-Ess\'{e}en theorem
states that if $Y_{1},Y_{2},...$ are independent identically distributed
random variables and $\rho =E[\left\vert Y_{i}^{3}\right\vert ]<\infty ,$
then, for all $n$%
\begin{equation*}
\sup_{y}\left\vert H_{n}(y)-\Phi (\frac{y}{\sigma })\right\vert <K\frac{\rho 
}{\sigma ^{3}\sqrt{n}}.
\end{equation*}%
The value of the constant given by Berry and Ess\'{e}en, $K=\frac{33}{4},$
has been improved and reduced to .7975 (van Beeck,1972). Thus the theorem
gives a bound on the effectiveness of the normal distribution in
approximating $H_{n}(.).$ A typical application of the theorem is in proving
the following. \vspace{0.5cm}\newline
\noindent \textbf{Theorem} If $\rho =E[\left\vert Y_{i}^{3}\right\vert
]<\infty $ then, as $n\rightarrow \infty ,$ 
\begin{equation}
\sup_{y}\left\vert H_{n}^{\ast }(y)-H_{n}(y)\right\vert \rightarrow 0
\label{p*-pformean}
\end{equation}%
for almost all sequences $y_{1},$ $y_{2},...$of independent observations
drawn from $F(.).$ \vspace{0.5cm}\newline
\noindent \textbf{Proof Outline.} By the Berry-Ess\'{e}en theorem we have 
\begin{equation*}
\left\vert P(H_{n}(y)-\Phi (\frac{y}{\sigma })\right\vert <K\frac{\rho }{%
\sigma ^{3}\sqrt{n}}
\end{equation*}%
and 
\begin{equation*}
\left\vert P^{\ast }(H_{n}^{\ast }(y)-\Phi (\frac{y}{\hat{\sigma}_{n}}%
)\right\vert <K\frac{\rho _{n}^{\ast }}{\hat{\sigma}_{n}^{3}\sqrt{n}}.
\end{equation*}%
By the strong law of large numbers $\mu ^{\ast }\equiv \bar{y}%
_{n}\rightarrow \mu ,$ $\hat{\sigma}_{n}\rightarrow \sigma ,$ $\rho
_{n}^{\ast }\rightarrow \rho $ all with probability 1. This shows that the
two probabilities in (\ref{p*-pformean}) converge to the same normal
distribution from which the result follows easily. $\square $ In fact the
above result does not depend on $\rho <\infty ;$ the condition $\sigma
^{2}<\infty $ is actually enough (see Bickel and Freedman, 1981 or Singh
1981.) However the condition $\rho <\infty $ and explicit use of the
Berry-Ess\'{e}en theorem is needed to establish the \emph{rate} of
convergence more precisely. In the remainder of this section we summarize
results obtained by Singh (1981) and Bickel and Freedman (1981). We need one
further definition. A random variable has distribution $F(.)$ that is \emph{%
lattice }if there is zero probability of it taking values outside the
discrete points $y_{j}=c+jd$ $j=0,1,2,...$ where $c$ and $d$ are constants. 
\vspace{0.5cm}\newline
\noindent \textbf{Theorem }For almost all sequences $Y_{1},$ $Y_{2},...$of
independent observations drawn from $F(.)$: \noindent (i) If $%
E(Y^{4})<\infty ,$ then 
\begin{eqnarray*}
&&\limsup_{n\rightarrow \infty }[\frac{\sqrt{n}}{\sqrt{\log \log n}}%
\sup_{y}\left\vert H_{n}^{\ast }(y)-H_{n}(y)\right\vert ]\ \ \  \\
&=&\frac{\sqrt{Var[(Y-\mu )^{2}]}}{2\sigma ^{2}\sqrt{\pi e}}
\end{eqnarray*}%
(ii) If $E[|Y|^{3}]<\infty $ and $F(.)$ is lattice then 
\begin{equation*}
\limsup_{n\rightarrow \infty }[\sqrt{n}\sup_{y}\left\vert \tilde{H}%
_{n}^{\ast }(y)-\tilde{H}_{n}(y)\right\vert ]=\frac{d}{\sqrt{2\pi }\sigma }
\end{equation*}%
(iii) If $E[|Y|^{3}]<\infty $ and $F(.)$ is non-lattice then 
\begin{equation*}
\sqrt{n}\sup_{y}\left\vert \tilde{H}_{n}^{\ast }(y)-\tilde{H}%
_{n}(y)\right\vert \rightarrow 0
\end{equation*}%
The result (i) shows that when $E(Y^{4})<\infty $ then the convergence rate
of $H_{n}^{\ast }$ is $O(\sqrt{\log \log n/n}).$ This is actually the same
as that for approximating $\sqrt{n}(\bar{Y}_{n}-\mu )/\hat{\sigma}_{n},$ the 
\emph{studentized} original $\bar{Y}$ by $Z,$ a standard normal, i.e.
approximating $H_{n}(y)$ by $\Phi (y/\hat{\sigma}_{n}).$ So in this case we
do no better using the bootstrap than standard normal theory. Results (ii)
and (iii) show that when $E[|Y|^{3}]<\infty $, then the convergence rate of $%
\tilde{H}_{n}^{\ast }(.)$ to $\tilde{H}_{n}(.)$ is at least $O(n^{-1/2})$
and is $o(n^{-1/2})$ when $F$ is non-lattice. Thus bootstrapping does better
than the usual normal approximation in this case. \emph{This is the key
theoretical result that underpins much of bootstrapping.} We consider now
quantile estimation. Perhaps the most informative general result is that due
to Singh (1981). Let $\eta =q(p)=F^{-1}(p);$ where the conditions of the
theorem ensure that the inverse is uniquely defined. We can estimate $\eta $
from the EDF using $\hat{\eta}=F_{n}^{-1}(p)=\sup \{y:$ $F_{n}(y)\leq p\}$
and define $\hat{\eta}^{\ast }=F_{n}^{\ast -1}(p)$ from $\mathbf{y}^{\ast }$
as usual. We take $H_{n}=P(\sqrt{n}(\hat{\eta}-\eta )\leq y)$ and $%
H_{n}^{\ast }=P^{\ast }(\sqrt{n}(\hat{\eta}^{\ast }-\hat{\eta})\leq y)$ 
\vspace{0.5cm}\newline
\noindent \textbf{Theorem }If $F$ has a bounded second derivative in a
neighborhood of $\eta $ and $f(\eta )=$ $dF/dy|_{\eta }>$ $0,$ then 
\begin{equation*}
\limsup_{n\rightarrow \infty }\frac{n^{1/4}\sup_{y}\left\vert H_{n}^{\ast
}(y)-H_{n}(y)\right\vert }{\sqrt{\log \log n}}=c_{p,F}\text{ with prob. 1}
\end{equation*}%
This result shows that $H_{n}^{\ast }(y)$ converges to $H_{n}(y)$ at rate $%
O(n^{-1/4}\log \log n).$ Now for a percentile estimator we have from the
Berry-Ess\'{e}en theorem 
\begin{equation*}
\sup_{y}\left\vert H_{n}(y)-\Phi (\frac{y}{\tau })\right\vert =O(n^{-1/2})
\end{equation*}%
where $\tau =\sqrt{p(1-p)}/f(\eta ).$ Thus if we were to use a normal
approximation for $H_{n}(y)$ we would use $\Phi (y/\hat{\tau})$ where $\hat{%
\tau}$ is an estimate of $\tau .$ Whether this is better than $H_{n}^{\ast
}(y)$ thus depends on the converence rate of $\hat{\tau},$ and the matter is
not a clear one.\pagebreak 

\textbf{\large 3.2 Asymptotic Accuracy of EDF's}\vspace{0.5cm}\newline
Edgeworth expansions are asymptotic series aimed at improving the normal
approximation by introducing additional terms that try to correct for
effects of skewness, kurtosis and higher moment which slow the rate of
convergence to normality. For fixed $n,$ asymptotic series usually diverge
as more and more terms are included. However for a fixed number of terms, $k$
say, the series converges as $n\rightarrow \infty .$ The rate of convergence
is usually of smaller order of than the last included term. We shall only
consider the special case $\hat{\eta}=\bar{Y}_{n}.$ Here, the general
Edgeworth expansion is a power series in $n^{-1/2}$ and has the form: 
\begin{eqnarray}
\tilde{H}_{n}(y) &=&\Phi (y)+n^{-1/2}p^{(1)}(y)\phi (y)+..  \label{Edgeworth}
\\
&&+n^{-k/2}p^{(k)}(y)\phi (y)+o(n^{-k/2})  \notag
\end{eqnarray}%
where $\phi (y)=(2\pi )^{-1/2}\exp (-y^{2}/2)$ is the standard normal
density, and $p_{j}$ is a polynomial of degree $3j-1.$ We have explicitly 
\begin{equation*}
p^{(1)}(y)=-\frac{1}{6}\kappa _{3}(y^{2}-1)
\end{equation*}%
and 
\begin{equation*}
p^{(2)}(y)=-\{\frac{1}{24}\kappa _{4}(y^{2}-3)+\frac{1}{72}\kappa
_{3}^{2}(y^{4}-10y^{2}+15)\}
\end{equation*}%
where $\kappa _{3}$ and $\kappa _{4}$ are the skewness and kurtosis of $F(.)$%
. The term involving $p^{(1)}$ corrects for the main effect of skewness, the
term involving $p^{(2)}$ corrects for the main effect of kurtosis and for
the secondary effect of skewness. Often the remainder term $o(n^{-k/2})$ can
be replaced by $O(n^{-(k+1)/2})$ when the Edgeworth expansion is said to be (%
$k+1)$th order accurate. Usually inclusion of more than one or two
correction terms becomes counter-productive as the coefficients associated
with the powers of $n^{-1/2}$ rapidly become large with $k$. When $%
E(|Y|^{3})<\infty $ and $Y$ is non-lattice then one-term Edgeworth
expansions for both $H_{n}(y)$ and $H_{n}^{\ast }(y)$ exist and a comparison
(see Shao and Tu, 1995, Section 3.3.3) shows that $H_{n}^{\ast }(y)$ has
smaller asymptotic mean square error than $\Phi (y/\hat{\sigma}_{n})$ unless
the skewness is zero. In fact comparison of $H_{n}^{\ast }(y)$ and the
one-term Edgeworth expansion estimator 
\begin{equation*}
H_{n}^{EDG}(y)=\Phi (z)+n^{-1/2}p_{n}^{(1)}(z\text{ }|\text{ }\mathbf{y}%
_{n})\phi (z)
\end{equation*}%
where $z=\hat{\sigma}_{n}^{-1}y,$ and $p_{n}^{(1)}(z$ $|$ $\mathbf{y}_{n})$
is the polynomial $p^{(1)}(z)$ with the moments of $F(.)$ replaced by the
sample moments calculated from $\mathbf{y}_{n},$ shows that both have the
same asymptotic mean square error. If we considered studentized versions the
bootstrap does even better. Under appropriate moment conditions both $\breve{%
H}_{n}(y)$ and $\tilde{H}_{n}^{\ast }(y)$ have two-term Edgeworth expansions
and a comparison of these shows that $\tilde{H}_{n}^{\ast }(y)$ has a
smaller asymptotic mean square error than the corresponding one-term
Edgeworth estimator, though they have the same convergence rate. The normal,
bootstrap and one-term Edgeworth expansion estimators of the standardized
distribution have been compared by Hall (1988) using the criterion of
asymptotic relative error. When $E(|Y|^{3})<\infty $ then the bootstrap does
better than the normal approximation, however it may or may not do better
than the one term Edgeworth expansion (see Shao and Tu, 1995). When $%
E(|Y|^{3})=\infty ,$ the situation is more complicated and depends on the
tail behaviour of $F(.).$ When the tail is thin the bootstrap can be worse
than the normal approximation. In estimating tail behaviour the bootstrap is
comparable to the one term Edgeworth expansion except in the extreme of the
tail.\pagebreak 

\textbf{\large 3.3 Asymptotic Accuracy of Confidence Intervals} \vspace{0.5cm%
}\newline
The above analysis focuses on distribution functions, and does not give the
whole picture. It is helpful to consider also the coverage accuracy of
confidence intervals. We shall write the basic confidence limit that we seek
as $\hat{\eta}_{\alpha }$ defined by 
\begin{equation*}
\Pr (\eta \leq \hat{\eta}_{\alpha })=\alpha
\end{equation*}%
and the normal, basic, percentile, studentized bootstrap, BC and BCa
approximations as $\hat{\eta}_{\alpha }^{Norm},$ $\hat{\eta}_{\alpha
}^{Boot},$ $\hat{\eta}_{\alpha ,}^{Per}$ $\hat{\eta}_{\alpha }^{Stu},$ $\hat{%
\eta}_{\alpha }^{BC}$ and $\hat{\eta}_{\alpha }^{BCa}$ respectively. We
summarise the results given in Shao and Tu (1995). These apply when the
parameter of interest is a smooth function of the mean, $\eta =\eta (\bar{y}%
_{n})$. Then an analysis analogous to that used for EDF's can be carried
out, but now relying on an expansion of the quantile, that is the inverse of
Edgeworth series, called the \emph{Cornish-Fisher expansion}. Let $\Phi
(z_{\alpha })=\alpha .$ Then, under appropriate moment conditions $\hat{\eta}%
_{\alpha }$ has an expansion of the form 
\begin{equation*}
q(\alpha )=z_{\alpha }+n^{-1/2}q^{(1)}(z_{\alpha })+n^{-1}q^{(2)}(z_{\alpha
})+o(n^{-1})
\end{equation*}%
where comparison with the Edgeworth exapnasion shows that 
\begin{equation*}
q^{(1)}(y)=-p^{(1)}(y)
\end{equation*}%
and 
\begin{equation*}
q^{(2)}(y)=p^{(1)}(y)p^{(1)\prime }(y)-\frac{1}{2}p^{(1)}(y)^{2}-p^{(2)}(y).
\end{equation*}%
Under appropriate conditions we find that analogous expansions exist for the
quantile approximations listed above. We find in particular that 
\begin{equation*}
\hat{\eta}_{\alpha }^{Boot}-\hat{\eta}_{\alpha }=O_{p}(n^{-1})
\end{equation*}%
and in general 
\begin{equation*}
\Pr (\hat{\eta}_{\alpha }^{Boot}\leq \eta )=1-\alpha +O(n^{-1/2}).
\end{equation*}%
However the two tailed version is second-order accurate: 
\begin{equation*}
\Pr (\hat{\eta}_{\alpha }^{Boot}\leq \eta \leq \hat{\eta}_{1-\alpha
}^{Boot})=1-2\alpha +O(n^{-1}).
\end{equation*}%
For symmetric distributions, these results apply to the percentile
approximation as well, for example: 
\begin{equation*}
\hat{\eta}_{\alpha }^{Per}-\hat{\eta}_{\alpha }=O_{p}(n^{-1}).
\end{equation*}%
The bootstrap BC method turns out to perform no better than the percentile
limit, in terms of convergence rate, but the constant factor is smaller so
it is marginally to be preferred. Studentization is definitely better with 
\begin{equation*}
\hat{\eta}_{\alpha }^{Stu}-\hat{\eta}_{\alpha }=O_{p}(n^{-3/2})\text{ and }%
\hat{\eta}_{\alpha }^{BCa}-\hat{\eta}_{\alpha }=O_{p}(n^{-3/2})
\end{equation*}%
and 
\begin{eqnarray*}
\Pr (\hat{\eta}_{\alpha }^{Stu} &\leq &\eta )=1-\alpha +O(n^{-1})\text{ } \\
&&\text{and} \\
\Pr (\hat{\eta}_{\alpha }^{BCa} &\leq &\eta )=1-\alpha +O(n^{-1}).
\end{eqnarray*}%
It follows that the two-sided intervals for both limits are also both
second-order accurate. Finally we note that 
\begin{equation*}
\hat{\eta}_{\alpha }^{Boot}-\hat{\eta}_{\alpha }^{Norm}=O_{p}(n^{-1})
\end{equation*}%
so that the usual normal approximation and the basic bootstrap behave
similarly.

\pagebreak

\textbf{{\large 3.4 \label{Failure}Failure of Bootstrapping}} \vspace{0.5cm}%
\newline
It should be clear from the previous three subsections that, for
bootstrapping to work well, regularity conditions are required on both the
distribution $F$ and also on the statistic of interest. More explicitly,
bootstrapping is sensitive to the tail behaviour of $F$; convergence of $%
H_{n}^{\ast }$ usually requires moment conditions on $F$ that are more
stringent than those needed for convergence of $H_{n}.$ Also the statistic $%
s(\mathbf{y})$ has to be suitably smooth in an appropriate sense. Finally it
is possible for convergence of the bootstrap to be sensitive to the method
used in carrying out the bootstrapping. An example of the first situation is
inconsistency of $s(\mathbf{y}),$ when it is simply the variance, even when
the asymptotic variance is finite (see Ghosh \emph{et al.}, 1984). This can
occur if $F(.)$ is fat-tailed and does not have appropriate moments. The
problem then arises because the bootstrap $s(\mathbf{y}^{\ast })$ can take
exceptionally large values. An example of the second situation is where $%
\mathbf{y}$ is a random sample, from $U(0,b)$ say, and we wish to consider $%
y_{(n)}$, the largest order statistic. Then a natural statistic to consider
is $s(\mathbf{y})=$ $n(b-y_{(n)})/b,$ which has a limiting standard
exponential distribution as $n\rightarrow \infty $. The bootstrap version is
then $s(\mathbf{y}^{\ast }\ |\ \mathbf{y})=$ $n(y_{(n)}-y_{(n)}^{\ast
})/y_{(n)}.$ But%
\begin{eqnarray*}
P^{\ast }(s(\mathbf{y}^{\ast }\ |\ \mathbf{y}) &=&0)=P^{\ast }(y_{(n)}^{\ast
}=y_{(n)}) \\
&=&1-(1-n^{-1})^{n}\rightarrow 1-e^{-1}.
\end{eqnarray*}%
Thus $H_{n}^{\ast }$ does not tend to $H_{n}$ as $n\rightarrow \infty .$
This result applies to any given order statistic $y_{(n-k)}$ where $k$ is
fixed. However the problem does not arise for a given quantile $y_{(pn)}$
where $p$ is fixed with $0<p<1.$ We shall see an example of the last
situation, where convergence depends not on the problem but on the
bootstraaping method employed, when we consider model selection in Section (%
\ref{Metamodelselect}).\pagebreak 

\textbf{{\large 4 \label{Paramodels}Monte-Carlo/Simulation Models}} \vspace{%
0.5cm}\newline
\textbf{\large 4.1 Direct Models} \vspace{0.5cm}\newline
Monte-Carlo or simulation models are \emph{direct models }in the sense that
they attempt to mimic the behaviour of an actual system by simulating the
different objects of a system and the (typically) dynamic relationships
between them. (The two names are interchangeable, but Monte-Carlo is the
name more favoured by statisticians, whereas simulation is more used by the
operational researchers.) A simple example is a single server queue we try
to capture the actual arrival patterns of customers and how they are then
processed by the server. The quantities that we try to analyse are summary
characteristics such as average queue length and waiting times. We can
therefore think of a set of $n$ simulation runs as yielding observations 
\begin{equation}
y_{j}=y(\mathbf{u}_{j},\mathbf{v}_{j},\mathbf{\hat{\theta}}(\mathbf{w}),%
\mathbf{x}_{j})\text{\ \ }j=1,2,...,n.  \label{ysim}
\end{equation}%
where the $y_{j}$ depend on a number of quantities that we now explain.
Firstly $\mathbf{u}_{j}$ denotes the stream of uniformly distributed $U(0,1)$
random numbers used in the $j$th run. Typically the uniforms are not used
directly, but are transformed into random variables drawn from distributions
other than the uniform. Next comes $\mathbf{v}_{j}$. This represents a
sequence of inputs that are random, but that has been generated
independently of the simulation model. A typical instance is where $\mathbf{v%
}_{j}$ comprises sampled real observations taken from some real system,
separate from the system being modelled, but on which $y_{j}$ depends. Such
a sampled real process is sometimes called a \emph{trace}. We shall simply
think of $\mathbf{v}_{j}$ as being just a sample of observations. In
addition there may be further quantities which may affect $y$. These are
denoted by $\mathbf{x}$ and $\mathbf{\theta }$. There is no essential
difference between $\mathbf{x}$ and $\mathbf{\theta }$ in the way that they
influence $y$. They are simply variables on which $y$ depends. However we
make a distinction in supposing that $\mathbf{x}$ are \emph{decision
variables}, i.e. quantities that can be selected by the simulator. However
we also include in $\mathbf{x}$ parameters whose values are known, and that
might affect $y$, but which are not selectable by the simulator. The reason
for adopting this convention is that it then allows us to assume $\mathbf{%
\theta }$ to be those parameter values, whose values are not known, and so
have to be estimated. We will therefore assume that in addition to $\mathbf{v%
}$, there exists input data $\mathbf{w}$ that is used exclusively to
estimate $\mathbf{\theta }$ and it is the estimated values, $\mathbf{\hat{%
\theta}}(\mathbf{w}),$ that are used in the simulation runs. A simple
example is a simulation model of a multiserver queue, where $y$ is the
observed average queue length over a given period, $\mathbf{v}$ might be a
previously observed set of interarrival times, $\mathbf{x}$ might be the
(scalar) number of servers and $\mathbf{\theta }$ the service rates of
servers. Here we treat $\mathbf{x}$ as being selectable by the simulator so
that it is a design variable, $\mathbf{\theta }$ may not be known and have
to be estimated from available sampled real service times, $\mathbf{w}$.
Resampling might already feature in carrying out the runs yielding the $%
y_{j} $ of (\ref{ysim}). For example variability in the $\mathbf{v}$ will
contribute to the variability of $y.$ We can therefore automatically build
this effect into the simulation runs simply by using resampling from an
initial given sequence $\mathbf{v}$ to construct resampled sequences $%
\mathbf{v}_{j}^{\ast }$ for use in the actual runs. Similarly in the case (%
\ref{ysim}), because $\mathbf{\hat{\theta}}(\mathbf{w})$ is estimated, we
can allow for its randomness affecting the distribution of $y$ by using 
\emph{independent values} of $\mathbf{\theta }_{j}$ in each simulation run.
When $\mathbf{\hat{\theta}}(\mathbf{w})$ is the ML estimate we can draw
sample values of $\mathbf{\theta :}$%
\begin{equation}
\mathbf{\theta }_{j}^{\ast }\sim N(\mathbf{\hat{\theta}},\text{ }I^{-1}(%
\mathbf{\hat{\theta}}))  \label{Thetastarnormal}
\end{equation}%
from the asymptotic normal distribution (\ref{Distnoftheta}) with $I(\mathbf{%
\hat{\theta}})$ calculated from (\ref{Ihat}). An alternative is to use
produce bootstrap samples $\mathbf{w}_{j}^{\ast }$ from $\mathbf{w},$ and to
use the bootstrap estimate 
\begin{equation}
\mathbf{\theta }_{j}^{\ast }=\mathbf{\hat{\theta}}(\mathbf{w}_{j}^{\ast })
\label{Thetastarboot}
\end{equation}%
for $\mathbf{\theta }$ in the $j$th run.

\textbf{\large 4.2 MetaModels} \vspace{0.5cm}\newline
We now consider a very different approach where the behaviour of a system is
represented by a \emph{metamodel. }This is simply where we regard the
behaviour of $y$ as representable by a \emph{statistical regression model}.
The runs are then not represented as (\ref{ysim}) but instead as 
\begin{equation*}
y_{j}=\eta (\mathbf{x}_{j};\mathbf{\beta })+\varepsilon _{j}\ \ j=1,2,...,n.
\end{equation*}%
where $\eta (\mathbf{x}_{j};\mathbf{\beta })$ is called the \emph{regression
function}. Its form is usually known, though in the problem of \emph{model
selection}, one has to choose from a number of possible competing models.
The errors $\varepsilon _{j}$ are assumed to be independent with $%
E(\varepsilon _{i})=0.$ We shall consider mainly the case where all the
error variances are equal $Var(\varepsilon _{i})=\sigma ^{2}$, but will
consider the heteroscedastic case too. The $\mathbf{\beta }$ are unknown
coefficients that have to be estimated. We consider linear metamodels
first.\pagebreak 

\textbf{\large 4.3 Linear Models} \vspace{0.5cm}\newline
In the linear metamodel, observations are assumed to take the form 
\begin{equation}
Y_{j}=\mathbf{x}_{j}^{T}\mathbf{\beta }+\varepsilon _{j}\text{ }\ \
j=1,2,...,n
\end{equation}%
where the errors are assumed to be independent and identically distributed
with $E(\varepsilon _{i})=0$ and $Var(\varepsilon _{i})=\sigma ^{2}$, and
the $\mathbf{\beta }$ are unknown and have to be estimated. Let 
\begin{equation*}
\mathbf{X}=(\mathbf{x}_{1},...,\mathbf{x}_{n})^{T},\text{ \ }\mathbf{Y}%
=(Y_{1},...,Y_{n})^{T}
\end{equation*}%
and 
\begin{equation*}
\hat{\beta}=(\mathbf{X}^{T}\mathbf{X})^{-1}\mathbf{X}^{T}\mathbf{Y}
\end{equation*}%
be the usual least squares estimate (equivalent to maximum likelihood when
the $\varepsilon _{i}$ are normally distributed). The fitted values are 
\begin{equation}
\mathbf{\hat{Y}=HY}  \label{HatEquation}
\end{equation}%
where 
\begin{equation}
\mathbf{H}=\mathbf{X}(\mathbf{X}^{T}\mathbf{X})^{-1}\mathbf{X}^{T}
\label{Hatmatrix}
\end{equation}%
is the well-known known 'hat' matrix. The raw residuals are $\mathbf{r}=(%
\mathbf{I-H})\mathbf{Y}$. One way of resampling is to sample from these
residuals as if they were the unknown errors. This gives the bootstrap
sample 
\begin{equation}
Y_{j}^{\ast }=\mathbf{x}_{j}^{T}\mathbf{\hat{\beta}}+r_{j}^{\ast }\text{ \ }%
j=1,2,...,n.  \label{Residualsampling}
\end{equation}%
We shall call this \emph{residual sampling. }However we have that $E(\mathbf{%
r})=\mathbf{0}$ and $Var(\mathbf{r})=\sigma ^{2}(\mathbf{I-H}).$ Thus a
better bootstrapping method is to sample from the \emph{adjusted residuals}%
\begin{equation}
e_{j}=r_{j}/(1-h_{jj})^{1/2}  \label{ei}
\end{equation}%
where $h_{jj}$ is the $j$th diagonal entry of $\mathbf{H}$ ($h_{jj}$
commonly known as the \emph{leverage} of the $j$th point). This form of
model-based resampling yields bootstrap samples of form: 
\begin{subequations}
\begin{equation}
Y_{j}^{\ast }=\mathbf{x}_{j}^{T}\mathbf{\hat{\beta}}+e_{j}^{\ast }\text{ \ }%
j=1,2,...,n.  \label{Bootregression}
\end{equation}%
We shall call this \emph{adjusted residual sampling.} An alternative is to
use \emph{case sampling} and to sample from the $(Y_{j},$ $\mathbf{x}_{j})$
pairs directly. The main problem with this method is that it introduces
extra variation into the data. In the conventional situation $\mathbf{X}$ is
regarded as fixed, but using case sampling replaces this by a bootstrap $%
\mathbf{X}^{\ast }$ which will be variable. This is not so much a problem
when the number of parameters, $p$ say, is small compared with $n$. A
partial correction can often be made. For example suppose we are interested
in the distribution of $\mathbf{c}^{T}\mathbf{\hat{\beta}}$ where $\mathbf{c}
$ is a constant vector. Then $Var[\mathbf{c}^{T}(\mathbf{\hat{\beta}}-%
\mathbf{\beta })]=\sigma ^{2}\mathbf{c}^{T}(\mathbf{X}^{T}\mathbf{X})^{-1}%
\mathbf{c}$. We can then study the behaviour of 
\end{subequations}
\begin{equation*}
\mathbf{c}^{T}(\mathbf{\hat{\beta}}-\mathbf{\beta })/(\mathbf{c}^{T}(\mathbf{%
X}^{T}\mathbf{X})^{-1}\mathbf{c}).
\end{equation*}%
indirectly by considering the bootstrap version 
\begin{equation*}
\mathbf{c}^{T}(\mathbf{\hat{\beta}}^{\ast }-\mathbf{\beta }^{\ast })/(%
\mathbf{c}^{T}(\mathbf{X}^{\ast T}\mathbf{X}^{\ast })^{-1}\mathbf{c})
\end{equation*}%
(where $\mathbf{\beta }^{\ast }=\mathbf{\hat{\beta}})$. A common variation
is when the errors are heteroscedastic so that their variances are unequal.
In this case resampling can be carried out as 
\begin{equation*}
Y_{j}^{\ast }=\mathbf{x}_{j}^{T}\mathbf{\hat{\beta}}+e_{j}t_{j}^{\ast }\text{
\ }j=1,2,...,n
\end{equation*}%
with $e_{i}$ as defined in (\ref{ei}) and with $t_{j}^{\ast }$ independent
and random with $E(t_{j}^{\ast })=0,$ $Var(t_{j}^{\ast })=1.$ Wu (1986)
suggests sampling the $t_{j}^{\ast }$ with replacement from the set 
\begin{equation*}
(r_{j}-\bar{r})/[n^{-1}\sum_{i=1}^{n}(r_{i}-\bar{r})^{2}]^{1/2}\ \
j=1,2,...,n.
\end{equation*}%
\textbf{\large \ }\pagebreak 

\textbf{\large 4.4 NonLinear Models} \vspace{0.5cm}\newline
The previous ideas apply with little change to nonlinear models. For
additive errors we have 
\begin{equation}
Y_{j}=\eta (\mathbf{x}_{j};\mathbf{\beta })+\varepsilon _{j}\text{ \ }%
j=1,2,...,n.  \label{Nonliny}
\end{equation}%
where $\eta (\mathbf{x}_{j};\mathbf{\beta })$ is not necessarily linear in $%
\mathbf{\beta }.$ Again $\mathbf{\hat{\beta}}$ can be obtained by ML say and
residuals formed: $r_{j}=Y_{j}-\eta (\mathbf{x}_{j};\mathbf{\hat{\beta}})$ \ 
$j=1,2,...,n.$ This time there is no obvious equivalent of standardization
so the bootstrap sample is simply%
\begin{equation*}
Y_{j}^{\ast }=\eta (\mathbf{x}_{j};\mathbf{\hat{\beta}})+r_{j}^{\ast }\text{
\ }j=1,2,...,n.
\end{equation*}%
However the method extends easily to more complex nonlinear situations. For
example it may be that we have observations $(y_{j},\mathbf{x}_{j})$ where 
\begin{equation*}
y_{j}\sim Poisson(\mu _{j}),\ \ \ \mu _{j}=\exp (\mathbf{x}_{j}^{T}\mathbf{%
\beta }).
\end{equation*}%
Again $\mathbf{\hat{\beta}}$ can be obtained by ML and a bootstrap sample
can then be formed by drawing a sample as 
\begin{equation*}
y_{j}^{\ast }\sim Poisson(\mu _{j}^{\ast }),\ \ \ \mu _{j}^{\ast }=\exp (%
\mathbf{x}_{j}^{T}\mathbf{\hat{\beta}}).
\end{equation*}%
A clear introduction to resampling using \emph{generalized linear models} is
given by Hjorth (1994).\pagebreak 

\textbf{\large 4.5 Uses of Metamodels} \vspace{0.5cm}\newline
We end by discussing some examples of \emph{why} we might wish to carry out
bootstrap resampling of a regression metamodel. We give three examples. The
first is when the regression function $\eta (\mathbf{x};\mathbf{\beta })$
represents a performance index and we might wish to construct a confidence
interval for it at some given value of $\mathbf{x},$ $\mathbf{x}_{0}$ say.
Suppose that we draw $B$ sets of bootstrap observations of the form (\ref%
{Bootregression}) 
\begin{equation*}
\{Y_{j}^{(i)\ast }=\eta (\mathbf{x}_{j};\mathbf{\hat{\beta}})+e_{j}^{(i)\ast
},j=1,2,...,n\},\ i=1,2,...,B,
\end{equation*}%
and from each set we calculate a bootstrap estimator $\mathbf{\hat{\beta}}%
^{(i)\ast }$ of $\mathbf{\hat{\beta}}$ using the ML method say. Then the
corresponding set of bootstrap regression function values $\eta (\mathbf{x}%
_{0},\mathbf{\hat{\beta}}^{(i)\ast })$ can be used to calculate a bootstrap
confidence interval. For example we could use (\ref{PercentileCI}) with $%
\mathbf{\hat{\eta}}^{\ast }$ in that formula having components $\hat{\eta}%
_{i}^{\ast }=\eta (\mathbf{x}_{0},\mathbf{\hat{\beta}}^{(i)\ast }).$ The
second example is when we wish to find an optimal setting for $\mathbf{x}.$
We consider the simplest case where 
\begin{equation*}
Y=\eta (x;\mathbf{\beta })+\varepsilon ,
\end{equation*}%
with $x$ scalar and where 
\begin{equation*}
\eta (x;\mathbf{\beta })=\beta _{0}+\beta _{1}x+\beta _{2}x^{2}.
\end{equation*}%
Then the optimal setting is given by $d\eta /dx=0,$ i.e. at $x_{opt}=-\beta
_{1}/(2\beta _{2}).$ Using ML to estimate this yields 
\begin{equation*}
\hat{x}_{opt}=-\hat{\beta}_{1}/(2\hat{\beta}_{2}).
\end{equation*}%
Again a simple confidence interval for the unknown $x_{opt}$ is given by (%
\ref{PercentileCI}), only this time, with $\mathbf{\hat{\eta}}^{\ast }$ in
that formula having components $\hat{\eta}_{i}^{\ast }=-\hat{\beta}%
_{1}^{(i)\ast }/(2\hat{\beta}_{2}^{(i)\ast }).$ This is an example where
bootstrapping furnishes a relatively easy answer to what in classical
statistics is a problem that is not straightforward. The final example
concerns the identification of important factors. Suppose we are interested
in identifying those coefficients in the regression function $\eta (\mathbf{x%
};\mathbf{\beta })$ for which $\left\vert \beta _{i}\right\vert >\beta _{0i}$
where $\beta _{0i}>0$ are given constants. Let $S=\{\beta _{i}:\left\vert
\beta _{i}\right\vert >\beta _{0i}\}$ denote the \emph{important set of
coefficients}. The obvious estimate is to select those coefficients $\beta
_{i}$ for which $\left\vert \hat{\beta}_{i}\right\vert >\beta _{0i},$i.e. $%
\hat{S}=\{\beta _{i}:\left\vert \hat{\beta}_{i}\right\vert >\beta _{0i}\}$.
Bootstrapping is a simple way of assessing the stability of this choice. We
generate $B$ bootstrap samples $(y_{j}^{(k)\ast },\mathbf{x}_{j}^{(k)\ast })$
$j=1,2,...,n,$ $k=1,2,...,B,$ using either residual sampling or case
sampling say and fit the regression model to each bootstrap sample to give
bootstrap estimates $\mathbf{\hat{\beta}}^{(k)\ast },\ k=1,2,...,B.$ We then
calculate $\hat{S}^{(k)\ast }=\{\beta _{i}:\left\vert \hat{\beta}%
_{i}^{(k)\ast }\right\vert >\beta _{0i}\},$ $k=1,2,...,K.$ Now assume that $B
$ is sufficiently large so that each distinct bootstrap important set that
has been obtained occurs a reasonable number of times. Then a $(1-\alpha )$
confidence region for the unknown true important set can be constructed by
selecting bootstrap important sets in decreasing order of their observed
frequency of occurrence until $(1-\alpha )100\%$ of the $\hat{S}^{(k)\ast }$
have been chosen. Related to identifying important factors in metamodels is
the rather more difficult problem of metamodel selection. We consider this
in the next section.\pagebreak 

\textbf{{\large 4.6 \label{Metamodelselect}Metamodel Comparison and
Selectionodels}} \vspace{0.5cm}\newline
Metamodel comparison and selection is a difficult subject. The main trouble
is that models that we wish to compare may be of different functional
complexity. The statistical properties associated with quantities derived
from different models may therefore be hard to put on a common scale on
which they can be compared. A reasonably satisfactory approach is based on
the use of \emph{cross-validation} technique. We suppose that the initial
data has the form 
\begin{equation*}
S=\{(y_{j},\mathbf{x}_{j}),\ \ j=1,2,...,n\}
\end{equation*}%
with 
\begin{equation*}
y_{j}=\eta (\mathbf{x}_{j},\mathbf{\beta })+\varepsilon _{j},\ \ j=1,2,...,n.
\end{equation*}%
Suppose that our fitted regression is 
\begin{equation*}
\hat{y}(\mathbf{x})=\eta (\mathbf{x},\mathbf{\hat{\beta}}).
\end{equation*}%
In its basic form cross-validation is used to assess how effective the
fitted regression is for predicting the response at some new design point $%
\mathbf{x}_{new}.$ Rather than explicitly choosing such a new point, a
simple idea is the \emph{leave-one-out} method where we drop an observed
point $(y_{j},\mathbf{x}_{j})$ from the set of all observed points, fit the
metamodel to the remaining points $S_{-j}=$ $S\smallsetminus (y_{j},\mathbf{x%
}_{j})$ giving the fitted regression as 
\begin{equation*}
\hat{y}_{-j}(\mathbf{x})=\eta (\mathbf{x},\mathbf{\hat{\beta}}_{-j}),
\end{equation*}%
and then look at the squared difference between the omitted $y_{j}$ and the
value of $y$ at $\mathbf{x}_{j}$, as predicted by the fitted model; i.e. 
\begin{equation*}
(y_{j}-\hat{y}_{-j}(\mathbf{x}_{j}))^{2}.
\end{equation*}%
If we do this even handedly by leaving out each observation in turn we have
as an \emph{estimate of cross-validation prediction error}: 
\begin{equation}
\hat{L}_{CV}=n^{-1}\sum_{j=1}^{n}(y_{j}-\hat{y}_{-j}(\mathbf{x}_{j}))^{2}.
\label{Lcv}
\end{equation}%
It turns out that, for the linear regression model, this formula simplifies
elegantly to one where we only need comparisons with the one model fitted to
all the original observations: 
\begin{equation*}
\hat{L}_{CV}=n^{-1}\sum_{j=1}^{n}\frac{(y_{j}-\hat{y}(\mathbf{x}_{j}))^{2}}{%
1-h_{jj}}
\end{equation*}%
where 
\begin{equation*}
\hat{y}(\mathbf{x}_{j})=\mathbf{x}_{j}^{T}\mathbf{\hat{\beta}},
\end{equation*}%
and $h_{jj}$ is the $j$th main diagonal entry in the hat-matrix (\ref%
{Hatmatrix}), as before. The distributional property of $\hat{L}_{CV}$ can
be obtained in the usual way by bootstrapping to get $B$ bootstrap samples.
If we use case resampling say then 
\begin{equation*}
S^{(i)\ast }=\{(y_{j}^{(i)\ast },\mathbf{x}_{j}^{(i)\ast }),\ \ j=1,2,...,n\}
\end{equation*}%
where each $(y_{j}^{(i)\ast },\mathbf{x}_{j}^{(i)\ast })$ is an observation
drawn at random from the set $S.$ We turn now to model selection. For
simplicity we shall only consider the linear case where $y=\mathbf{x}^{T}%
\mathbf{\beta }+\varepsilon $, though with an obvious adjustment, the
discussion does generalize. Let $\mathbf{\beta }$ be of dimension $p$. If we
are uncertain as to which design variables are really needed in the model,
then we would have in principle up to $2^{p}$ models to choose from with $%
\binom{p}{q}$ (sub)models where there were precisely $q$ covariates
selected. We denote a typical submodel by $M$. The estimate of prediction
error using (\ref{Lcv}) is 
\begin{equation*}
\hat{L}_{CV}(M)=n^{-1}\sum_{j=1}^{n}\frac{(y_{j}-\hat{y}_{M}(\mathbf{x}%
_{j}))^{2}}{1-h_{Mjj}}.
\end{equation*}%
It turns out that this measure does not work as well as it might even when $n
$ is large. (This is an example previously referred to in subsection (\ref%
{Failure}) where inconsistency arises not in the problem itself but in the
bootstrap sampling method.) A much better variant is not to leave out just
one observation at a time but instead to split the observations into two: a 
\emph{training} set, and an \emph{assessment} set with respectively $%
n_{t}=n-m$ and $n_{a}=m$ observations in each. We shall moreover select not
one but $K$ such pairs and denote the sets as $S_{t,k}$ and $S_{a,k}$, $%
k=1,2,...,K.$ We shall write $\mathbf{\hat{\beta}}_{Mk}$ for the
coefficients of model $M$ fitted to the $k$th training set, and write $\hat{y%
}_{Mjk}=\mathbf{x}_{Mj}^{T}\mathbf{\hat{\beta}}_{Mk}$ for the value of $%
\mathbf{x}_{j}^{T}\mathbf{\beta }$ predicted by this model $M$ at $\mathbf{x}%
_{j}.$ Then $\hat{L}_{CV}(M)$ becomes 
\begin{equation}
\hat{L}_{CV}(M)=K^{-1}\sum_{k=1}^{K}m^{-1}\sum_{j\in S_{a,k}}(y_{j}-\hat{y}%
_{Mjk})^{2}.  \label{Lcvmhat}
\end{equation}%
We use the same set of $K$ pairs for all models being compared. \ Provided $%
n-m\rightarrow \infty $ and $m/n\rightarrow 1$ as $n\rightarrow \infty $
then selecting $M$ to minimize (\ref{Lcvmhat}) will yield a consistent
estimator for the correct model. When $n$ is small it may not be possible to
select $m$ large enough in which case Davison and Hinkley (1997) suggest
taking $m/n\simeq 2/3.$ The variability of $\hat{L}_{CV}(M)$ can be
estimated by bootstrapping in the usual way.\pagebreak 

\textbf{{\large 5 \label{Comparisons}Bootstrap Comparisons}} \vspace{0.5cm}%
\newline
In this Section we consider problems where there are a number of different
samples to compare. We suppose that we have $m$ data sets%
\begin{equation}
\mathbf{y}_{i}=(y_{i1},y_{i2},...,y_{i,n_{i}}),\ \ i=1,2,...,m
\label{Severalys}
\end{equation}%
the $i$th being of size $n_{i}$ runs. This situation covers two different
scenarios in particular. The first is where we are considering the question:
Does the output of the simulation model accurately represent the output of
the system being modelled? Here we have just two data sets, so that $m=2,$
and one data set comprises observations of a real system whilst the other
comprises the observed output from a simulation model. Thus this is a
question of validation. The second is when we have a number of different
simulation models to assess. Here validation against real data is not the
issue. The different models simply represent different systems, and their
validity is not in doubt. Instead we are just interested in assessing how
differently the systems behave, as represented by the output of their
simulation models. Typically we might be interested in which system produces
the greatest average output, or least average delay. Alternatively we might
wish to compare output variability. A similar methodology can be applied to
either problem. We discuss this in the remainder of this section.\pagebreak 

\textbf{\large 5.1 Goodness-of-Fit and Validation} \vspace{0.5cm}\newline
A discussion of validation involving trace-driven simulation is described in
Kleijnen \emph{et al.} ( 2000, 2001). The basic idea used there can be
generalized. We treat the problem as essentially one of goodness-of-fit. A
simple starting point is where we have $n$ simulation outputs $y_{j},$ $%
j=1,2,...,n,$ to compare with $m$ observations $y_{j}^{\text{\emph{Real}}},$ 
$j=1,2,...,m$ of a real system and we wish to know if the two samples are
identically distributed. A good goodness-of-fit statistic is the two sample
Cramer - von Mises statistic (see Anderson, 1962): 
\begin{equation}
W^{2}=\int [F_{n}(y)-F_{m}^{\text{\emph{Real}}}(y)]^{2}dH_{n+m}(y)
\label{Cvm}
\end{equation}%
where $F_{n},$ $F_{m}^{\text{\emph{Real}}}$ and $H_{n+m}$ are respectively
the EDF's of the simulated, real and combined samples. The asymptotic
distribution of $W^{2}$ is known when the samples are drawn from continuous
distributions, but it is easy to set up a bootstrap version of the test that
can handle discrete distributions just as well. We can use a bootstrapping
method as follows. We obtain $B$ pairs of bootstrap samples $(\mathbf{y}%
^{(i)\ast },$ $\mathbf{y}^{\text{\emph{Real}}(i)\ast })$, $i=1,2,...,B$,
each sample in the pair being obtained by resampling with replacement from $%
\mathbf{y}$ and $\mathbf{y}^{\text{\emph{Real}}}$ combined but with $\mathbf{%
y}^{(i)\ast }$ the same sample size as $\mathbf{y}$, and $\mathbf{y}^{\text{%
\emph{Real}}(i)\ast }$ the same sample size as $\mathbf{y}^{\text{\emph{Real}%
}}$. Denote the EDFs of the samples of each pair by $F_{n}^{(i)\ast }$ and $%
F_{n}^{\text{\emph{Real}}(i)\ast },$ $i=1,2,...,B$. We can now calculate $B$
bootstrap two sample Cramer - von Mises statistics from each pair: 
\begin{equation}
W^{(i)\ast 2}=\int [F_{n}^{(i)\ast }(y)-F_{m}^{\text{\emph{Real}}(i)\ast
}(y)]^{2}dH_{n+m}^{(i)\ast }(y).  \label{Cvmboot}
\end{equation}%
Under the null assumption that $\mathbf{y}$ and $\mathbf{y}^{\text{\emph{Real%
}}}$ are drawn from the same distribution it follows that each $W^{(i)\ast 2}
$ is just a bootstrap version of $W^{2}$. We can thus calculate a critical
p-value from the EDF of the $W^{(i)\ast 2},$ $i=1,2,...,B$. The null
hypothesis that $\mathbf{y}$ and $\mathbf{y}^{\text{\emph{Real}}}$ are drawn
from the same distribution can be tested simply by checking if the original $%
W^{2}$ exceeds this p-value or not. The validation method just described is
easily extended to allow the representational accuracy of a number of
different competing simulation models to be compared against (the same) real
data. We order the Cramer von-Mises goodness of fit statistics (\ref{Cvm})
obtained from comparing the real data with the simulated output of each of
the models. We can assess the reliability of the comparison by bootstrapping
as in (\ref{Cvmboot}) to obtain bootstrap distributions of each such Cramer
von-Mises statistic and looking at the degree of overlap of amongst these
distributions. When the $W^{2}$ statistic shows the data samples $\mathbf{y}%
_{j}$ to be significantly different, there is an interesting decomposition
of $W^{2}$ that allows the nature of the differences to be more clearly
identified . Durbin and Knott (1972) show that in the one sample case where $%
F_{n}(.)$ is being compared with a uniform null distribution, so that $%
W^{2}=n$ $\int [F_{n}(y)-y]^{2}dy,$ then $W^{2}$ has the orthogonal
representation%
\begin{equation*}
W^{2}=\sum_{j=1}^{\infty }(j\pi )^{-2}z_{nj}^{2}
\end{equation*}%
where the $z_{nj}$ are scaled versions of the coefficients in the Fourier
decomposition of the process$\sqrt{n}$ $(F_{n}(y)-y).$ The $z_{nj}$ are
stochastic of course, depending on $F_{n}(.),$ but they are independent and,
under the null, have identical distributions. It can be shown that $z_{n1},$ 
$z_{n2}$ and $z_{n3}$ are dependent essentially exclusively on deviations
respectively of the mean, variance and skewness of $F()$ from that of the
null. In other words these components $z_{n1},$ $z_{n2}$ and $z_{n3}$ can be
used to provide convenient statistical tests for these differences. Cheng
and Jones (2000, 2004) describe a generalisation of the results of Durbin
and Knott (1972), which they term \emph{EDFIT statistics,} suitable for
comparison of data of the form (\ref{Severalys}) arising in the simulation
context. Their formulation uses ranks rather than the original. Though this
leads to some loss of power the method does then have the advantage of
enabling critical values of tests to be easily obtained by bootstrapping,
and moreover in an exact way.\pagebreak 

\textbf{\large 5.2 Comparison of Different Systems} \vspace{0.5cm}\newline
We now consider the situation of (\ref{Severalys}) where the data sets are
outputs from different simulation models only, and no comparison is made
with real data. Here we are interested simply in making comparisons between
different models. The discussion of the previous subsection goes through
with no essential change. The only difference is that all samples have the
same status; there is no sample being singled out as special by being drawn
from a real system. The EDFIT approach can therefore be used directly, with
bootstrapping providing critical null values. A more direct approach, not
using goodness of fit ideas, is possible. We focus on examining differences
between the means, $\bar{y}_{i},$ $i=1,2,..,m,$ of the $m$ samples of (\ref%
{Severalys}). Comparison of other sample statistics be handled in the same
way. If we suppose that largest is best we can rank the means as $\bar{y}%
_{(1)}>\bar{y}_{(2)}>...>\bar{y}_{(m)}.$ The question then is how stable is
this order? Bootstrapping provides an easy answer. We generate $B$ bootstrap
sets of observations%
\begin{eqnarray}
\mathbf{y}_{i}^{(k)\ast } &=&(y_{i1}^{(k)\ast },y_{i2}^{(k)\ast
},...,y_{i,n_{i}}^{(k)\ast }),\ \   \label{Severalyboots} \\
i &=&1,2,...,m,\ \ k=1,2,...,B  \notag
\end{eqnarray}%
where each sample $\mathbf{y}_{i}^{(k)\ast },\ k=1,2,...,B,$ is obtained by
sampling with replacement from $\mathbf{y}_{i}.$ We then order the means in
each bootstrapped set: $\bar{y}_{(1)}^{(k)\ast }>\bar{y}_{(2)}^{(k)\ast
}>...>\bar{y}_{(m)}^{(k)\ast }$, $k=1,2,...,B.$ The frequency count of how
many times the mean of a given sample comes out on top is a measure its
relative merit. A more comprehensive picture is provided by setting up a
two-way table showing the number of times $\bar{y}_{i}^{(k)\ast }>\bar{y}%
_{j}^{(k)\ast }$ out of the $B$ bootstrapped sets, for each possible pair $%
1\leq i,$ $j\leq m$. The parametric form of the bootstrapping procedure is
equally easy to implement, though it is not clear there is much advantage to
be had in doing so. Suppose for example that the $i$th sample $\mathbf{y}_{i}
$ is drawn from the distribution $F^{(i)}(.,\mathbf{\theta }_{i}).$ Usually
the $F^{(i)}(.,\mathbf{\theta }_{i})$ will have the same functional form,
for example all normal distributions, but the procedure works equally well
when the $F^{(i)}$ are functionally different. Let the mean of the $F^{(i)}$
distribution be $\mu ^{(i)}(\mathbf{\theta }_{i}).$ Then we can estimate, by
ML say, the parameters of each distribution from their corresponding sample.
Let these estimates be $\mathbf{\hat{\theta}}_{i}.$ We can then carry out
parametric bootstrapping by forming (\ref{Severalyboots}) but this time with
each sample $\mathbf{y}_{i}^{(k)\ast },\ k=1,2,...,B,$ obtained by sampling
from the fitted distribution $F^{(i)}(.,\mathbf{\hat{\theta}}_{i}).$ The
analysis then proceeds as before.\pagebreak 

\textbf{\large 6 Bayesian Models} \vspace{0.5cm}\newline
Much of the current popularity of Bayesian methods comes about because
increased computing power makes possible Bayesian analysis by numerical
procedures, most notably Markov Chain Monte Carlo (MCMC). See Gilks et al.
(1996).This allows numerical sampling to be carried out in much the same way
as is done in bootstrap and other resampling procedures. We indicate the
commonality by considering just one or two situations where Bayesian methods
and resampling overlap. We again focus on a random sample $\mathbf{w}=(w_{1},
$ $w_{2},$ $...,$ $w_{n})$ drawn from a distribution $F(w,$ $\mathbf{\theta }%
).$ As in the situation where we considered ML estimation, the form of $F$
is known but it depends on a vector $\mathbf{\theta }$ of parameters. In the
ML estimation situation $\mathbf{\theta }_{0},$the true value of $\mathbf{%
\theta },$ was assumed unknown. In the Bayesian case a prior distribution is
assumed known. We consider just the continuous case and denote the pdf of
the prior by $\pi (\mathbf{\theta }).$ The main step in Bayesian analysis is
to construct the posterior distribution $\pi (\mathbf{\theta }$\ $|\ \mathbf{%
w})$ which shows how the sample $\mathbf{w,}$ which depends $\mathbf{\theta }%
,$ has modified the prior. The Bayesian formula is 
\begin{subequations}
\begin{equation}
\pi (\mathbf{\theta }\ |\ \mathbf{w})=\frac{p(\mathbf{w\ }|\ \mathbf{\theta }%
)\pi (\mathbf{\theta })}{\int p(\mathbf{w\ }|\ \mathbf{\theta })\pi (\mathbf{%
\theta })d\mathbf{\theta }}.  \label{Bayestheorem}
\end{equation}%
The difficult part is the evaluation of the normalizing integral $\int p(%
\mathbf{w\ }|\ \mathbf{\theta })\pi (\mathbf{\theta })d\mathbf{\theta }.$
MCMC has proved remarkably successful in providing a powerful numerical
means of doing this. In the context of simulation a Bayesian approach is
useful in assessing uncertainty concerning input parameter values used in a
simulation model. We can adopt the viewpoint given in (\ref{ysim}) and
regard the runs as taking the form: 
\end{subequations}
\begin{equation}
y_{j}=y(\mathbf{u}_{j},\mathbf{v}_{j},\mathbf{\theta },\mathbf{x}_{j})\text{%
\ \ }j=1,2,...,n.  \label{ysim2}
\end{equation}%
only now $\mathbf{\theta }$ is assumed to have a Bayesian prior distribution 
$\pi (\mathbf{\theta })$. Note that in this situation we do \emph{not} have
the equivalent of data $\mathbf{w}$ from which to calculate a posterior
distribution for $\mathbf{\theta }$. What we can do is to treat the prior $%
\pi (\mathbf{\theta })$ as \emph{inducing a prior distribution} on $y=y(%
\mathbf{u},\mathbf{v},\mathbf{\theta },\mathbf{x}).$ In this sense we can
think of $y$ itself as having a prior which can then be estimated by
sampling $\mathbf{\theta }$ from its prior $\pi (\mathbf{\theta }),$
yielding values $\mathbf{\theta }_{j}$ $j=1,2,...,n,$ and then running the
model to produce a set of observations 
\begin{equation}
y_{j}=y(\mathbf{u}_{j},\mathbf{v}_{j},\mathbf{\theta }_{j},\mathbf{x}_{j})%
\text{\ \ }j=1,2,...,n.
\end{equation}%
The EDF of these $y_{j}$ then estimates this prior distribution of $y.$ This
process clearly is the analogue to the classical situation where $\mathbf{%
\theta }$ is estimated from data $\mathbf{w,}$ as is supposed in (\ref{ysim}%
), and we then allowed for this variability in the $y_{j}$ by replacing $%
\mathbf{\hat{\theta}}(\mathbf{w})$ by $\mathbf{\theta }_{j}^{\ast }$
calculated from (\ref{Thetastarnormal}) or (\ref{Thetastarboot}). More
interestingly we can take this a step further if there are real observations
available of the $y$ process itself. We do not attempt a fully general
formulation but give an example to indicate the possibilities. Suppose that 
\begin{equation*}
y_{j}^{\text{\emph{Real}}}=y^{\text{\emph{Real}}}(\mathbf{x}_{j}),\ \ \ \
j=1,2,...,n
\end{equation*}%
comprise $n$ observations on the real system being modelled by the
simulation. The precise relationship between the simulation output $y=y(%
\mathbf{u},\mathbf{v},\mathbf{\theta },\mathbf{x})$ and $y^{\text{\emph{Real}%
}}(\mathbf{x})$ is not known. However we might assume that 
\begin{equation*}
y(\mathbf{u},\mathbf{v},\mathbf{\theta },\mathbf{x})\sim N(y^{\text{\emph{%
Real}}}(\mathbf{x}),\ \sigma ^{2})
\end{equation*}%
where $\sigma ^{2}$ is an additional parameter that will also be treated as
Bayesian in the sense of having a prior. We can express our great prior
uncertainty about $\sigma $ by assuming a \emph{reference prior
distribution, }$\rho (\sigma ),$ for it. Then, by (\ref{Bayestheorem}), the
posterior distribution of $(\mathbf{\theta },\sigma )$ is proportional to 
\begin{equation*}
\sigma ^{-n}\pi (\mathbf{\theta })\rho (\sigma )\exp \{-\frac{1}{2\sigma ^{2}%
}\sum_{j=1}^{n}[y(\mathbf{u},\mathbf{v},\mathbf{\theta },\mathbf{x}_{j})-y^{%
\text{\emph{Real}}}(\mathbf{x}_{j})]^{2}\}.
\end{equation*}%
The posterior distribution can thus be obtained by MCMC methods for example.
An interesting application of this problem occurs in epidemiological
modelling. Suppose that $y$ is a measure of the progress of an epidemic that
is dependent on factors $\mathbf{\theta }$ for which there are previous
measurements or for which there is expert information. A Bayesian approach
is then a natural way of incorporating this prior information. We need also
to have a good epidemic model for producing a simulated $y$. Thus reverse
use of simulation as indicated above has allowed this prior information to
be updated.\pagebreak 

\textbf{{\large 7 \label{Timeseries}Time Series Output}} \vspace{0.5cm}%
\newline
Bootstrapping of time series is a well studied problem. In simulation the
most likely use of such procedures is to generate correlated input for a
model. As usual the parametric form is relatively easy to explain and
implement and we discuss this first.

\textbf{\large 7.1 Residual Sampling} \vspace{0.5cm}\newline
We consider the case where the time-series is an autoregressive model. Here
the residual sampling method used to construct a bootstrap metamodel applies
with little change. Suppose we have the autorgressive model 
\begin{equation*}
Y_{t}=\sum_{j=1}^{p}a_{j}Y_{t-j}+\varepsilon _{t}
\end{equation*}%
where the $\varepsilon _{t}$ are independently \ and identically
distributed, commonly called the \emph{innovations. }Suppose that $%
y_{1},y_{2},...,y_{n}$ are a series drawn from this model. Then we can
estimate the $a_{j}$ by least squares say, yielding the estimates $\hat{a}%
_{j},$ $j=1,2,...,p,$ and form the residuals 
\begin{equation*}
r_{t}=y_{t}-\sum_{j=1}^{p}\hat{a}_{j}y_{t-j},\;\;t=p+1,p+2,...,n.
\end{equation*}%
We can then form the bootstrap time-series as 
\begin{equation*}
y_{t}^{\ast }=\sum_{j=1}^{p}\hat{a}_{j}y_{t-j}^{\ast }+r_{t}^{\ast
},\;\;t=1,2,...,n
\end{equation*}%
by sampling the $r_{t}^{\ast }$ from the EDF of the residuals $\{r_{t}\}.$
We need $y_{1}^{\ast },y_{2}^{\ast },...,y_{p}^{\ast }$ to initiate the
process, but if we assume that the observed series is stationary, it is
probably easiest to simply initiate the series with some arbitrary starting
values, possibly the original $y_{1},y_{2},...,y_{p},$ then run the
bootstrapping sufficiently long until initial effect are negligible and
collect the actual $y_{t}^{\ast }$ from that point on. Freedman (1984) gives
conditions for the asymptotic validity of this procedure, Basawa \emph{et al.%
} (1989) extending these results to the case of nonlinear
time-series.\pagebreak 

\textbf{\large 7.2 Block Sampling} \vspace{0.5cm}\newline
For time-series the analogue of case sampling is \emph{block sampling.} We
cannot sample individual observations $y_{t}$ because this loses the
correlation between observations. If the series is long enough then we can
take $n=bl$ and think of the series as comprising $b$ blocks each of length $%
l.$ We write the $i$th block as $\mathbf{y}%
_{i}=(y_{l(i-1)+1},y_{l(i-1)+2},...,y_{(li)})$ $i=1,2,...,b.$ Bootstrapping
is done by sampling blocks with replacement from this set of $b$ blocks,
retaining the order of the observations in each block when writing down the
individual observations of the bootstrapped series. A balance needs to be
struck between having block lengths long enough to retain the correlation
properties between neighboring observations, and having enough blocks to
measure the inherent variation of the series. A typical compromise is to use
say $b=l=n^{1/2}$ so that both quantities tend to infinity as $n\rightarrow
\infty .$ A major weakness of block sampling is the loss of correlation
incurred by the random sampling of blocks. This loss of correlation is
called \emph{whitening}. It is especially serious when the statistic of
interest involves correlations of high lag. The crude block sampling just
described may be quite ineffective if the size of blocks is not large
enough, because calculation involves quantities which straddle blocks and
which are then not sufficiently correlated because of the whitening. There
are many variants of block sampling aimed at reducing the effect of
whitening in specific situations. A good example is estimation of the lag $m$
covariance 
\begin{equation*}
c_{m}=\frac{1}{n-m}\sum_{t=1}^{n-m}(y_{t}-\bar{y})(y_{t+m}-\bar{y}).
\end{equation*}%
Here we can define a two-dimensional process 
\begin{equation*}
\mathbf{z}_{t}=\left( 
\begin{array}{c}
z_{1t} \\ 
z_{2t}%
\end{array}%
\right) =\left( 
\begin{array}{c}
y_{t} \\ 
y_{t+m}%
\end{array}%
\right) ,\;\;t=1,2,..,n-m
\end{equation*}%
with $\bar{z}_{i}=(n-m)^{-1}\sum_{t=1}^{n-m}z_{it}$ and think of $c_{1}$ as
a statistic of this process 
\begin{equation*}
c_{1}=\frac{1}{n-1}\sum_{t=1}^{n-1}(z_{1t}-\bar{z}_{1})(z_{2t}-\bar{z}_{2}).
\end{equation*}%
We can then obtain bootstrap $\mathbf{z}_{t}^{\ast }$ by sampling with
replacement from the set $\{\mathbf{z}_{t}\}.$The bootstrap lag $m$
covariance then clearly substantially retains the covariance of the original
series as we have, in effect, bootstrap sampled the terms $(y_{t}-\bar{y}%
)(y_{t+m}-\bar{y})$ appearing in the formula giving $c_{m}.$ Generalizations
of this technique are known as \emph{block of blocks sampling}.\pagebreak 

\textbf{\large 7.3 Spectral resampling} \vspace{0.5cm}\newline
Residual and block sampling are time domain techniques. An alternative
approach is to sample in the frequency domain. A big advantage is that
spectral increments are uncorrelated, and for Gaussian processes this
strengthens to independent increments. Suppose we have $n=2m+1$ observations 
\begin{equation*}
y_{t},\;t=-m,-m+1,...,m-1,m
\end{equation*}%
for which there is a continuous spectral density $S(\omega )$ and define the
frequencies $\omega _{k}=2\pi k/n,$ $-m\leq k\leq m.$ Then the observations
have the spectral representation 
\begin{equation*}
y_{t}=\sum_{k=-m}^{m}a_{k}e^{i\omega _{k}t}
\end{equation*}%
where 
\begin{equation*}
a_{k}=n^{-1}\sum_{t=-m}^{m}y_{t}e^{-i\omega _{k}t}.
\end{equation*}%
In this section $i=\sqrt{-1}.$ For a Gaussian process the real and purely
imaginary components of the $a_{k}$ are independent, and the $a_{k}$ are
asymptotically so. Norgaard (1992) gives two possible ways of obtaining
bootstrap samples of the $a_{k.}$ The simplest version is to draw $%
a_{k}^{\ast }$ at random from one of the twelve elements 
\begin{equation}
(\pm a_{k-1},\;\pm a_{k},\;\pm a_{k+1},\;\pm ia_{k-1},\;\pm ia_{k},\;\pm
ia_{k+1})
\end{equation}%
when $0<k<n$ and to draw $a_{n}^{\ast }$ at random from one of the twelve
elements 
\begin{equation}
(\pm a_{n-1},\;\pm a_{n},\;\pm a_{n},\;\pm ia_{n-1},\;\pm ia_{n},\;\pm
ia_{n}).
\end{equation}%
(note the repetition of some elements in this latter case). In both cases we
set 
\begin{equation*}
a_{-k}^{\ast }=\overline{a_{k}^{\ast }}
\end{equation*}%
i.e. $a_{-k}^{\ast }$ is the complex conjugate of $a_{k}^{\ast }.$ The value
of $a_{0}^{\ast }$ needs to be real. The simplest case is when $E(Y_{t})=0$
when we can select $a_{0}^{\ast }$ at random as one of the four elements $%
(\pm a_{0},\;\pm |a_{1}|).$ A variant which seems to be significantly
superior is called the \emph{extended circle} (EC) method by Norgaard
(1992). Here we select $|a|_{k}^{\ast }$ as a random element of the set $%
(|a_{k-1}|,\;|a_{k}|,\;|a_{k+1}|)$ and then give this a random spin in both
the real and imaginary directions: 
\begin{equation*}
a_{k}^{\ast }=|a|_{k}^{\ast }(\cos \phi _{1k}+i\sin \phi _{2k}),\;k=1,2,...,n
\end{equation*}%
where $\phi _{1k}$ and $\phi _{2k}$ are independent $U(-\pi ,\pi )$ angles.
We have $a_{-k}^{\ast }=\overline{a_{k}^{\ast }}$ as before.\pagebreak 

\textbf{\large 8 Final Comment}\vspace{0.5cm}\newline
There are many topics not covered in this appendix that could well have been
included. We have for instance, not touched on the closely related technique
of jackknifing. Other topics we have made only cursory mention, such as
cross validation and when bootstrapping fails.\pagebreak 

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\end{document}
