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Simulation: a modeler's approach
Journal of Statistical Planning and Inference 91 (2000) 169–170
www.elsevier.com/locate/jspi
Book review Simulation: a modeler’s approach J. Thompson; Wiley, New York, 306 pages, price US$84.95, ISBN 0-471-25184-4 This book details an interesting approach to simulation. In the preface, the author deÿnes a simulation as “the generation of pseudo-data on the basis of a model, a database, or the use of a model in the light of a database.” The author feels that many statisticians mistakenly develop oversimpliÿed models to make them mathematically tractable, and then they rely on the computer as a big calculator to solve the artiÿcial problems which result. In a di erent direction, his philosophy is to promote the synergy between data, models and the computer in order to simulate a less simpliÿed analogue to our perception of the real world. It is hard to argue against this paradigm shift, but it may be a long time coming in a ÿeld which appears to value mathematical eloquence more than solutions to real-world problems. Books such as this may help in this process by providing examples which accentuate the true power of the computer as a simulation tool. While the author’s notion of a simulation may be somewhat di erent from what others have in mind, the author still covers the basic simulation concepts discussed in other texts on the subject. Chapter 1 provides a brief description of the traditional methods for generating pseudo-random numbers, and Chapters 2 and 3 cover the standard Monte Carlo methods for quadrature and for ÿnding the solutions of di erential equations. Chapter 4 discusses a wide range of topics related to simulation solutions for Markov and Poisson processes as well as linear equations. In Chapter 5, the author makes a great case for his approach to simulation. The SIMDAT and SIMEST algorithms are introduced as tools for estimating model parameters via simulation. SIMDAT is a smooth resampling algorithm, and SIMEST is a method for estimating model parameters using goodness of ÿt statistics in conjunction with pseudo-data from SIMDAT. At the end of the chapter, an oncological example is given which illustrates the pitfalls of the typical modeling approach and highlights the use of these algorithms. The remaining chapters provide interesting simulation examples from a variety of ÿelds. Chapter 6 discusses simulation techniques for evaluating “what if ” scenarios for stock and derivative models. Chapter 7 develops simulation methodology for otherwise impractical multivariate procedures in statistical process control. In Chapter 8, simulation is used to show chaotic processes can be smoothed by the addition of c 2000 Elsevier Science B.V. All rights reserved. 0378-3758/00/$ - see front matter PII: S 0 3 7 8 - 3 7 5 8 ( 0 0 ) 0 0 0 9 0 - 2
170
Book review / Journal of Statistical Planning and Inference 000 (2000) 169–170
noise, and in turn one may then obtain models which better ÿt reality. The role of simulation in Bayesian statistics is developed in Chapter 9 with an example featuring the EM algorithm, data augmentation and the Gibbs Sampler. Chapter 10 examines the development of resampling techniques from a very historical perspective starting with Fisher. In Chapter 11, the author proposes a mean update algorithm as an alternative to multivariate nonparametric density estimation in higher dimensions. Lastly in Chapter 12, the author examines the likely e ects of public policy decisions on the growth of the AIDS epidemic in the ÿrst world. The author’s literary style and keen sense of history makes the manuscript an interesting read. While one might expect a book on simulation to be light on mathematics, this is not true in general for this book as the mathematical level varies widely from chapter to chapter. The author does manage to discuss a number of statistical techniques which have been hot research topics over the last two decades. These include the bootstrap, the Gibbs Sampler and empirical likelihood. The connection of these advanced statistical procedures with real world data and interesting models is perhaps the best feature of this work. Overall, the author has produced a book which should serve as an excellent reference for applied statisticians from a wide range of research ÿelds. Webster West Department of Statistics, University of South Carolina Columbia, SC 29208, USA
www.elsevier.com/locate/jspi
Book review Simulation: a modeler’s approach J. Thompson; Wiley, New York, 306 pages, price US$84.95, ISBN 0-471-25184-4 This book details an interesting approach to simulation. In the preface, the author deÿnes a simulation as “the generation of pseudo-data on the basis of a model, a database, or the use of a model in the light of a database.” The author feels that many statisticians mistakenly develop oversimpliÿed models to make them mathematically tractable, and then they rely on the computer as a big calculator to solve the artiÿcial problems which result. In a di erent direction, his philosophy is to promote the synergy between data, models and the computer in order to simulate a less simpliÿed analogue to our perception of the real world. It is hard to argue against this paradigm shift, but it may be a long time coming in a ÿeld which appears to value mathematical eloquence more than solutions to real-world problems. Books such as this may help in this process by providing examples which accentuate the true power of the computer as a simulation tool. While the author’s notion of a simulation may be somewhat di erent from what others have in mind, the author still covers the basic simulation concepts discussed in other texts on the subject. Chapter 1 provides a brief description of the traditional methods for generating pseudo-random numbers, and Chapters 2 and 3 cover the standard Monte Carlo methods for quadrature and for ÿnding the solutions of di erential equations. Chapter 4 discusses a wide range of topics related to simulation solutions for Markov and Poisson processes as well as linear equations. In Chapter 5, the author makes a great case for his approach to simulation. The SIMDAT and SIMEST algorithms are introduced as tools for estimating model parameters via simulation. SIMDAT is a smooth resampling algorithm, and SIMEST is a method for estimating model parameters using goodness of ÿt statistics in conjunction with pseudo-data from SIMDAT. At the end of the chapter, an oncological example is given which illustrates the pitfalls of the typical modeling approach and highlights the use of these algorithms. The remaining chapters provide interesting simulation examples from a variety of ÿelds. Chapter 6 discusses simulation techniques for evaluating “what if ” scenarios for stock and derivative models. Chapter 7 develops simulation methodology for otherwise impractical multivariate procedures in statistical process control. In Chapter 8, simulation is used to show chaotic processes can be smoothed by the addition of c 2000 Elsevier Science B.V. All rights reserved. 0378-3758/00/$ - see front matter PII: S 0 3 7 8 - 3 7 5 8 ( 0 0 ) 0 0 0 9 0 - 2
170
Book review / Journal of Statistical Planning and Inference 000 (2000) 169–170
noise, and in turn one may then obtain models which better ÿt reality. The role of simulation in Bayesian statistics is developed in Chapter 9 with an example featuring the EM algorithm, data augmentation and the Gibbs Sampler. Chapter 10 examines the development of resampling techniques from a very historical perspective starting with Fisher. In Chapter 11, the author proposes a mean update algorithm as an alternative to multivariate nonparametric density estimation in higher dimensions. Lastly in Chapter 12, the author examines the likely e ects of public policy decisions on the growth of the AIDS epidemic in the ÿrst world. The author’s literary style and keen sense of history makes the manuscript an interesting read. While one might expect a book on simulation to be light on mathematics, this is not true in general for this book as the mathematical level varies widely from chapter to chapter. The author does manage to discuss a number of statistical techniques which have been hot research topics over the last two decades. These include the bootstrap, the Gibbs Sampler and empirical likelihood. The connection of these advanced statistical procedures with real world data and interesting models is perhaps the best feature of this work. Overall, the author has produced a book which should serve as an excellent reference for applied statisticians from a wide range of research ÿelds. Webster West Department of Statistics, University of South Carolina Columbia, SC 29208, USA