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Simulation methodology for statisticians, operations analysts and engineers
376
Book Reviews
- I find the way certain notions are explained sometimes confusing. E.g., stationarity is defined in the chapter on no-trend series. It is suggested by the choice of an example of a stationary time series that all autoregressive time series are nonstationary. - The number of statistical tests presented to test for the presence or absence of phenomenona such as trend, randomness, seasonals is so abundant that it is not always clear to the reader which test to use. - So-called updating procedures such as exponential smoothing are introduced without giving the rationale of the underlying stochastic time series models. It is, therefore, difficult for the inexperienced student to appreciate the difference between the 'fixed parameter' models, e.g., regression analysis, and the 'stochastic' parameter models which lead to exponential smoothing and similar techniques. It should be mentioned that this criticism does not apply to the chapter on BoxJenkins methods which in my view is one of the best balanced chapters in the book. The book contains some errors but these are not annoying.
J. BOAS Unileoer Research Laboratory P.O. Box 114 3130 A C Vlaardingen The Netherlands
P.A.W. LEWIS and E.J. ORAV Simulation Methodology for Statisticians, Operations Analysts and Engineers
Chapman and Hall, London, 1989, vi + 416 pages, £18.95 Intended as an advanced level text for students of Statistics, Operational Research and Engineering, this is clearly a major work which has been compiled in a painstakingly thorough way. It is the first of two volumes, and covers the basic concepts behind modelling and random number generation, and also such topics as variance reduction and the comparison of simulations. (Volume II, not reviewed here, is even more advanced and goes on to consider systems simulation, the analysis of dependent output, and the generation of
random variables and random stochastic processes.) The first four chapters provide an introduction to simulation for those with only a minimal background in statistics and probability theory. Following the definition of simulation in Chapter 1, Chapter 2 offers six 'Golden Rules' of Simulation. Practitioners will be disappointed that no mention is made here of involving the problem owner from conception to implementation. Perhaps this omission reflects the book's theoretical approach, neglecting the process of doing simulation project work and instead just doing experiments. Chapter 3 presents five illustrative examples and problems; these include single and multiple server single-input queues, the trimmed t statistic, the reliability of series systems, and a military proportional navigation problem. It concludes with some very brief comments about stability, convergence and experimentation. Crude (or Straightforward) Simulation and Monte Carlo is the title of Chapter 4. Here, a formal consideration of pseudo-random numbers is followed by a worked example on the passage of ships through a mined channel. This is quite superb; the results are displayed graphically, and one somewhat surprising outcome is established and explained. Chapter 5 goes on to cover uniform pseudo-random variable generation and expose the cyclic repetitivity that some random number generators exhibit. Bit stripping on computers is introduced, and several generators are discussed in some detail. Some of these are mapped against specific software packages. The remaining chapters require further knowledge of these areas plus stochastic processes and computer programming; they are aptly grouped under the heading Sophisticated Simulation. Chapters 6 and 7 consider methods for analysing single batches of data, and design and comparison techniques are discusses in Chapter 8. Techniques such as sectioning, jackknifing and bootstrapping are presented in Chapter 9, bivariate problems in Chapter 10, and variance reduction in Chapter 11. A large number of example applications are included in this second part of the volume, and the emphasis throughout is on graphical methods for analysing results. The absence of any detailed mention of Visual Interactive Modelling Packages such as Hocus, Genetik, or Witness is surprising [ll-
Book Reviews In the preface, the authors state that their rationale behind the content and style of the book is that they feel that the basic emphasis should be on the statistical and modelling techniques. Accepting that, the-work is of the highest quality and completeness. However, to any practitioners there will be a concern that the emphasis is not squarely on problem solving; indeed their concentration on statistics to the exclusion of simulation itself will put off many practitioners and, in spite of the title, virtually all engineers.
377
Reference [1] Bell, P.C. (1989), "Stochastic visual interactive simulation
models", Journalof the OperationalResearch Society 40/7, 615-624.
A.M. TOBIAS Department of Engineering Production University of Birmingham Edgbaston Birmingham B15 2TT UK
Book Reviews
- I find the way certain notions are explained sometimes confusing. E.g., stationarity is defined in the chapter on no-trend series. It is suggested by the choice of an example of a stationary time series that all autoregressive time series are nonstationary. - The number of statistical tests presented to test for the presence or absence of phenomenona such as trend, randomness, seasonals is so abundant that it is not always clear to the reader which test to use. - So-called updating procedures such as exponential smoothing are introduced without giving the rationale of the underlying stochastic time series models. It is, therefore, difficult for the inexperienced student to appreciate the difference between the 'fixed parameter' models, e.g., regression analysis, and the 'stochastic' parameter models which lead to exponential smoothing and similar techniques. It should be mentioned that this criticism does not apply to the chapter on BoxJenkins methods which in my view is one of the best balanced chapters in the book. The book contains some errors but these are not annoying.
J. BOAS Unileoer Research Laboratory P.O. Box 114 3130 A C Vlaardingen The Netherlands
P.A.W. LEWIS and E.J. ORAV Simulation Methodology for Statisticians, Operations Analysts and Engineers
Chapman and Hall, London, 1989, vi + 416 pages, £18.95 Intended as an advanced level text for students of Statistics, Operational Research and Engineering, this is clearly a major work which has been compiled in a painstakingly thorough way. It is the first of two volumes, and covers the basic concepts behind modelling and random number generation, and also such topics as variance reduction and the comparison of simulations. (Volume II, not reviewed here, is even more advanced and goes on to consider systems simulation, the analysis of dependent output, and the generation of
random variables and random stochastic processes.) The first four chapters provide an introduction to simulation for those with only a minimal background in statistics and probability theory. Following the definition of simulation in Chapter 1, Chapter 2 offers six 'Golden Rules' of Simulation. Practitioners will be disappointed that no mention is made here of involving the problem owner from conception to implementation. Perhaps this omission reflects the book's theoretical approach, neglecting the process of doing simulation project work and instead just doing experiments. Chapter 3 presents five illustrative examples and problems; these include single and multiple server single-input queues, the trimmed t statistic, the reliability of series systems, and a military proportional navigation problem. It concludes with some very brief comments about stability, convergence and experimentation. Crude (or Straightforward) Simulation and Monte Carlo is the title of Chapter 4. Here, a formal consideration of pseudo-random numbers is followed by a worked example on the passage of ships through a mined channel. This is quite superb; the results are displayed graphically, and one somewhat surprising outcome is established and explained. Chapter 5 goes on to cover uniform pseudo-random variable generation and expose the cyclic repetitivity that some random number generators exhibit. Bit stripping on computers is introduced, and several generators are discussed in some detail. Some of these are mapped against specific software packages. The remaining chapters require further knowledge of these areas plus stochastic processes and computer programming; they are aptly grouped under the heading Sophisticated Simulation. Chapters 6 and 7 consider methods for analysing single batches of data, and design and comparison techniques are discusses in Chapter 8. Techniques such as sectioning, jackknifing and bootstrapping are presented in Chapter 9, bivariate problems in Chapter 10, and variance reduction in Chapter 11. A large number of example applications are included in this second part of the volume, and the emphasis throughout is on graphical methods for analysing results. The absence of any detailed mention of Visual Interactive Modelling Packages such as Hocus, Genetik, or Witness is surprising [ll-
Book Reviews In the preface, the authors state that their rationale behind the content and style of the book is that they feel that the basic emphasis should be on the statistical and modelling techniques. Accepting that, the-work is of the highest quality and completeness. However, to any practitioners there will be a concern that the emphasis is not squarely on problem solving; indeed their concentration on statistics to the exclusion of simulation itself will put off many practitioners and, in spite of the title, virtually all engineers.
377
Reference [1] Bell, P.C. (1989), "Stochastic visual interactive simulation
models", Journalof the OperationalResearch Society 40/7, 615-624.
A.M. TOBIAS Department of Engineering Production University of Birmingham Edgbaston Birmingham B15 2TT UK