Elements of Computational Statistics by James E. Gentle

Elements of Computational Statistics

Features

Cover Type: Hard Cover with 440 pages

Published by: Springer; 1st. ed. 2002. Corr. 2nd printing edition June 28, 2005

Written in: English

ISBN 10 Number: 0387954899

ISBN 13 Number: 978-0387954899

Book Dimensions: 9.3 x 6 x 1 inches

Weighs: 1.4 pounds

Product Review

From the reviews:

TECHNOMETRICS

"For the probable purchasers of this text, I feel that Gentle has succeeded in presenting a broad overview of the major areas of modern computational statisticsIn conclusion, I found this book to be a comprehensive summary of computational methods used in modern statistical analyses. It certainly has a place on my bookshelf. The bibliography alone makes it a valuable research tool for those working in this area."

SHORT BOOK REVIEWS

"This book describes many of the exciting, even revolutionary, developments in computational statistics which have been made over the last two or three decadesThe book has a rather mainstream statistical feel to it: it gives great discussions of topics such as bootstrap methods, density function estimation, and multivariate tools such as principle components, clustering and projection pursuitIt would provide an great grounding for someone beginning to work in this area"

"The book by James Gentle illustrates statistical ideas and computational tools to explore, extract, and test for significance the information in collected data. The chapters have exercises and solutions. The book is suitable to be a text book in a graduate level course on computational statistics. I enjoyed reading and recommend very highly to the statistical community." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 75 (2), 2005)

"This book provides a wealth of knowledge on the topic of computational statistics . Gentles prose is very readable, with many sections written in an almost conversational style. I highly recommend this book as a resource . I wish to commend Gentle for his efforts on this well-written book. The vast coverage of methodology makes this book a valuable resource for any statistician involved with computational statistics as well as for applied researchers in other fields who use advanced statistical methods." (Herbert K. H. Lee, Journal of the American Statistical Association, Vol. 98 (463), September, 2003)

"Computational statistics is a collection of methods and techniques in statistics which are computationally intensive and use the computer as a tool for experimentation. The material covered is extensive. relevant references are given. The book also contains lots of exercises of varying level . The writing style in this book is accessible . Practical aspects are stressed. All in all, the book is valuable for people who want to know something about the strength and applicability of statistical methods ." (Dr. G. Jongbloed, Kwantitatieve Methoden, Issue 72B28, 2004)

"The book is devoted to computationally intensive methods of statistical analysis such as resampling, randomization tests or data mining. the book covers a lot of questions . So, this may be a good reference guide on the current state of statistics. The bibliography contains more than 500 items and there are many WWW references in the text." (R. E. Maiboroda, Zentralblatt MATH, Vol. 1031, 2004)

"Gentle defines computational statistics as the class of statistical methods characterized by computational intensity and the supporting methods for such methods. There is good coverage here of an extensive range of statistical methods . Each chapter is accompanied by a good selection of challenging exercises . clear descriptions of the basics together with several references to advanced topics for the interested reader. I will be happy to use it to dip into as a general reference book." (Richard Bolton, Journal of Applied Statistics, Vol. 31 (9), 2004)

"This book grew out of courses on computational statistics that were offered by the author at George Mason University. The exercises are an important way of adding to the information that is gained from the text. The presentation is very accessible. Apart from its obvious use as a course text, this is a useful reference for any statistician who uses or wishes to use computationally intensive methods. This is the third of a series . I have enjoyed reading all of them." (David Kemp, Journal of the Royal Statistical Society, Vol. 157 (3), 2004)

"This book is an audacious undertaking by the author an effort to present all of the major statistical methods that require a large degree of computational intensity. I feel that Gentle has succeeded in presenting a broad overview of the major areas of modern computational statistics. I found this book to be a comprehensive summary of computational methods used in modern statistical analyses. It certainly has a place on my bookshelf. The bibliography alone makes it a valuable research tool ." (William J. Owen, Technometrics, Vol. 45 (3), 2003)

"This book describes many of the exciting, even revolutionary, developments in computational statistics which have been made over the last two or three decades. it gives great discussions of topics such as bootstrap methods, density function estimation, and multivariate tools such as principal components, clustering and projection pursuit. It would provide an great grounding for someone beginning to work in this area ." (D. J. Hand, Short Book Reviews, Vol. 23 (1), 2003)

Product Description
Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of computational statistics involve resampling, partitioning, and multiple transformations of a dataset. They may also make use of randomly generated artificial data. Implementation of these methods often requires advanced techniques in numerical analysis, so there is a close connection between computational statistics and statistical computing. This book describes techniques used in computational statistics, and addresses some areas of application of computationally intensive methods, such as density estimation, identification of structure in data, and model building. Although methods of statistical computing are not emphasized in this book, numerical techniques for transformations, for function approximation, and for optimization are explained in the context of the statistical methods. The book includes exercises, some with solutions. The book can be used as a text or supplementary text for various courses in modern statistics at the advanced undergraduate or graduate level, and it can also be used as a reference for statisticians who use computationally-intensive methods of analysis. Although some familiarity with probability and statistics is assumed, the book reviews basic methods of inference, and so is largely self-contained. James Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He has held several national offices in the American Statistical Association and has served as associate editor for journals of the ASA as well as for other journals in statistics and computing. He is the author of Random Number Generation and Monte Carlo Methods and Numerical Linear Algebra for Statistical Applications.

Reader Reviews
At first I thought this was a revision of his excellent book with Kennedy on statistical computing. But after browsing it I discovered it was a book on a subject that is near and dear to my "computationally intensive statistical methods". I then discovered a whole chapter on bootstrap methods, a topic of have studied, taught and written about! I concur with the editorial reviewer on the content of the book. So I will not go into a detailed description that would just be repetitious. The distinction that Gentle chooses to make between statistical computing and computational statistics is interesting. He sees statistical computing as methods of calculation. So statistical computing encompasses numerical analysis methods, Monte Carlo integration etc. On the other hand computational statistics involves computer-intensive methods like bootstrap, jackknife, cross-validation, permutation or randomization tests, projection pursuit, function estimation, data mining, clustering and kernel methods. But Gentle includes some other tools that are not necessarily intensive such as transformations, parametric estimation and some graphical methods. Where would you put the EM algorithm and Markov Chain Monte Carlo? These are computational algorithms and hence I think belong under statistical computing, but they also can be computationally intensive methods especially MCMC. What does Gentle say. Well Chapter 1 is on preliminaries and he includes a section on the role of optimization in statistical inference. Here the EM algorithm is well placed as well as many other computing techniques like iteratively reweighted least squares, Lagrange multipliers and quasi-Newton methods. The bootstrap chapter provides a self-contained introduction to the topic supported by a good choice of references. Variance estimation and the various types of bootstrap confidence intervals for parameters are discussed. Independent samples are the main topic though section 4.4 briefly describes dependency cases such as in regression analysis and time series. The book is up-to-date and authoritative and is a very good choice for anyone interested in computer-intensive methods and its connections to statistical computing. This is the way modern statistics is moving and so is worth looking at. Comment | | (Report this)

List Price: $84.95
Available from Amazon
Price: $74.40
Updated on 6-4-2008.