Russell Cheng's Home Page

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Contact Information

Faculty of Mathematical Studies, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom.

Tel: +44 (0)23 8059 4550 (direct), 8059 5155 (OR secretaries)

Fax: +44 (0)23 8059 5147




Brief CV


Russell C.H. Cheng is Emeritus Professor of Operational Research in the Faculty of Mathematical Studies, Southampton University, UK.


He has an M.A. and the Diploma in Mathematical Statistics from Cambridge University, England. He obtained his Ph.D. from Bath University. He was Lecturer and then Senior Lecturer in Operational Research in the Department of Mathematics, UWIST, Cardiff, from 1972 to 1988; and Reader at Cardiff University from 1988 to 1994. He was Professor of Operational Research in the University of Kent at Canterbury, England from 1994 to 1998. He was Professor of Operational Research in Southampton University  School of Mathematics from 1999 to 2008 andHead of OR Group (from Oct1999- Sept2008), Deputy Dean, Faculty of Mathematical Studies, 2002-2003, Deputy Head (Research) of the School of Mathematics 2003-2004

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 Institute of Mathematics and Its Applications, Member of the Operational Research Society.

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.

Commentary on Publication list:

1. Computer Generation of Random Variables

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.

If you want cookbook recipes then try my Chapter on Random Variate Generation. This appears in A7 Handbook of Simulation, edited by Jerry Banks and published by Wiley in 1998.

 2. Design and Analysis of Computer Simulation Experiments

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 Davenport contains theoretical results on the rates of convergence of stratification techniques, showing their dependence on the dimensionality of the problem as well as discussing practical implications.

Most recently I have worked with a research student, Traylor, and a colleague, Holland, on sensitivity analysis of simulation output. The papers [b6,b10,b11,b12,b13, B7,B8] have already appeared. In the work with Holland we have applied our methods to examples involving analysis of the performance of computer and communication networks. These areas of application are currently under study.

I have also been working on sensitivity issues with Prof Jack Kleijnen (Tilburg University) and with Prof Viatcheslav Melas (St Petersburg State University) and in the area of Statistical Design of Simulation Experiments. A paper with Kleijnen is due out quite soon in Operations Research, and one with Melas and Kleijnen in J. Statistical Planning and Inference.

3. Construction of Confidence Bands

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.

4. Estimation in Non-Regular Problems

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 Kent and several papers are in the pipeline. I am currently writing a book with Steve on this topic. 

5. Optimal Control of Jump Processes

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.

 6. Computer Generated Imagery Techniques and Marine Simulation

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

These include:

gamma distribution (any of up to 3 pars)

Johnson SU (up to 4 pars)

log-gamma distribution ( up to 3 –pars)

normal distribution

Pearson distribution all types

Weibull distribution ( up to 3-pars)

In Version14 also:

            Johnson SB


            Johnson SU


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



GTP Bootstrap


NATCORSimulationCourse   Version: July 2013.

NATCORSimulationCourse   Version: July 2015.


Box-Jenkins Method for ARIMA Models Using X12 from Excel.