Russell Cheng's Home Page
[Last changed:30 June 2015: This is index15.html]
Faculty of Mathematical Studies,
Tel: +44 (0)23 8059 4550 (direct), 8059 5155 (OR secretaries)
Fax: +44 (0)23 8059 5147
Russell C.H. Cheng is Emeritus Professor of Operational Research in the
Faculty of Mathematical Studies,
He has an M.A. and the Diploma in Mathematical Statistics
Additionally he has acted as consultant in
the area of marine simulation, and was Research Director of Norcontrol Imaging
Systems Ltd from 1988-1991. He is a former Chairman of the U.K. Simulation
Society, a Fellow of the Royal Statistical Society, Fellow of the
His current research interests include: design and analysis of simulation experiments, variance reduction methods and parametric estimation methods and he has over a hundred publications in these and related areas.
He was a former Associate Editor of the ACM Transactions on Modeling and Computer Simulation and for Management Science and Editorial board member of the American Journal of Mathematical and Management Sciences, He was a Founding Editor of the IMA Journal of Management Mathematics. He is currently a technical board member of Simulation News Europe, an Editorial Advisory Committee member of the ACM Transactions on Modeling and Computer Simulation, and an Editorial Board member of the Journal of Simulation.
Research Interests up to 2002
I have divided my Publication List into the six main areas that I have been interested in. My main current areas of interest are 2. Design and Analysis of Computer Simulation Experiments, and 4. Estimation in Non-Regular Problems
Here is a brief description of each. Click on linked items to jump straight to them in the list.
From 1976 to 1984 there was considerable interest in the development of computer methods of generating random variables from various probability distributions, notably the so-called gamma and beta distributions. The better current implementations date from that time, but be warned there are still books being written, and implementations using outmoded, demonstrably inferior, methods from before then. If you want something neat and simple then [A1,A2] and those [A3,A4] with Feast ( a research assistant at the time) are methods that I am most pleased with.
I have several current research interests in this area: optimal design, selection of input models, validation and sensitivity analysis.
The papers [B2, B3] jointly with Feast were the first to investigate the use of control variables under normality assumptions. This is a common assumption nowadays.
The method of antithetic variates is well known in theory, but is not so easy to apply in practice. The papers [b1,b2 B4,B5] develop a practical methodology for its application from a novel viewpoint. Paper [b3], is expository, but suggests a structure for the methodology of variance reduction. The conference paper [a1][, gives an implementation of quasi-random generators.
The papers [A5, A6], though concerned with variate generation, would find application in variance reduction In particular [A5] contains a result concerning the decomposition of inverse Gaussian variates.
Paper [b4], is concerned with some unusual ways of doing stratified sampling.
Paper [B6] with
Most recently I have worked with a
research student, Traylor, and a colleague,
I have also been working on sensitivity
issues with Prof Jack Kleijnen (
The basic idea of constructing confidence bands dates back to the work of Working and Hotelling in 1927. However the idea can be extended non-trivially. Paper [C2] with Iles gives convenient closed form formulas for a problem a number of well known distributions. Papers [C3,C5,c1,c2] describe some of the work done for the MoD in this area, as well as applications to materials handling. The methodology developed in paper [C3] is capable of wide application.
This is technically the most demanding area that I have worked on. It concerns a number of problems in which the well-known and the normally powerful method of maximum likelihood can fail to provide estimators with good properties. The papers [D1,D2,D3,D4,D5,D6] address a notorious outstanding problem where the likelihood blows up and such estimators are inconsistent. The joint paper [D2] with Amin (a research student at the time) gives what I consider to be a natural and practical solution using spacings that is demonstrably superior to previous suggestions. The joint paper [D4] to some extent gives a unifying explanation of why certain methods work and why others do not for such problems. Paper [D6] proves a remarkable property of the spacings method that has uses in goodness-of-fit testing. The joint papers [D7,D8] tackle a second problem of non-regularity that occurs in estimation and in curve fitting respectively. Each paper gives a new characterization of the difficulty, and each shows how the problem can be fully solved. This work lead to the a read paper to the Royal Statistical Society [D10].
Papers [D9, D11] is recent work on this with Steve Liu from
The papers [E1,E2],[e1] and those [E3,E4] jointly with Jones (then a part-time student) attempt to model the operation and control of large scale chemical plants using Markov Decision Processes. The purpose is to develop a practical solution to a problem of industrial interest which is analytically intractable except in simplistic rather trivial cases. Paper [E2] establishes, in fair theoretical detail, conditions under which the technique can be expected to work. As [E3] and [E4] go on to show, the practical results agree with the theory. The topic has been relatively unexplored, with what published work being dispersed.
Relatively recently I returned to the problem and [E5] contains a detailed theoretical analysis using the theory of maximal differential equations.
I have developed a number of computer algorithms over several years in my consultancy work on applications of computer generated imagery in marine simulation. These have had real and I held two Teaching Company Grants from the EPSRC in this field; each Scheme included a specific research component in addition to the usual commercial ones. Some results of this work are reported in [f2, f3, f4].
Perhaps more interesting are the papers [F1, F2]. Paper [F1], though short, gives a new rendering method that is demonstrably two to three times faster than previously suggested methods. Paper [F2] gives a comprehensive solution to the problem of fast anti-aliasing. Both methods have been used in several extremely successful commercial systems.
Research and Teaching Interests from 2002, Mainly Connected with Items Involving Various Excel Utilities with VBA code :
Fitting Univariate Distributions:
This link, FittingDistributions, is to an Excel Workbook with VBA modules that can fit various different continuous univariate distributions.
Updated version of Excel Workbook, 17 May 2015: Version14
gamma distribution (any of up to 3 pars)
Johnson SU (up to 4 pars)
log-gamma distribution ( up to 3 –pars)
Pearson distribution all types
Weibull distribution ( up to 3-pars)
In Version14 also:
Link to Notes accompanying the workbook: Notes on Distribution Fitting
The workbook can also assess the goodness of fit using the Anderson-Darling test. The critical points are calculated for the model being fitted using bootstrap resampling.
This App is based on my WSC2011 paper downloadable here Cheng2011
Winter Simulation Conference 2009 Introductory Tutorial on Resampling Methods of Analysis in Simulation Studies:
Here is a link to the Proceedings paper with live links: WSC09CC5
Here is a link to a Word file with the text set out in presentation slide format, with active links: WSC09SlideFormatVersion
Fitting Finite Mixture Distributions. The paper Cheng & Currie 2013 discusses a Bayesian approach to this problem using Importance Sampling. There is an accompanying App in which the approach is implemented. The application is run by an Excel interface. The interface and application in zipped form can be downloaded from here: FineMix. This is a folder containing all that is needed to use the App. It includes a brief guide.
Some Teaching Materials
NATCORSimulationCourse Version: July 2013.
NATCORSimulationCourse Version: July 2015.