Elements of Computational Statistics [Hardcover]

James E. Gentle (Author)

Book Description

ISBN-10: 0387954899 | ISBN-13: 978-0387954899 | Publication Date: August 12, 2002

Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

Editorial Reviews

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 statistics…In 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 decades...The book has a rather mainstream statistical feel to it: it gives excellent discussions of topics such as bootstrap methods, density function estimation, and multivariate tools such as principle components, clustering and projection pursuit…It would provide an excellent 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 … . Gentle’s 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 fundamentals 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 excellent 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 excellent grounding for someone beginning to work in this area … ." (D. J. Hand, Short Book Reviews, Vol. 23 (1), 2003)

From the Back Cover

This book describes techniques used in computational statistics and considers some of the areas of applications, such as density estimation and model building, in which computationally intensive methods are useful. In computational statistics, computation is viewed as an instrument of discovery; the role of the computer is not just to store data, perform computations, and produce graphs and tables, but additionally to suggest to the scientist alternative models and theories. Another characteristic of computational statistics is the computational intensity of the methods; even for datasets of medium size, high performance computers are required to perform the computations. Graphical displays and visualization methods are usually integral features of computational statistics.

Product Details

• Hardcover: 440 pages
• Publisher: Springer (August 12, 2002)
• Language: English
• ISBN-10: 0387954899
• ISBN-13: 978-0387954899
• Product Dimensions: 9.3 x 6 x 1 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #529,095 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews

26 of 26 people found the following review helpful:

5.0 out of 5 stars more than just statistical computing, January 21, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Elements of Computational Statistics (Hardcover)

This is basically a review I wrote of this book nearly six years ago. I read the book with great interest. As six years have past there has definitely been a continual growth in the speed, memory capabilities and size of modern computers. So books like this may be obsolete and should be revised.

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 me, "computationally intensive statistical methods". I then discovered a whole chapter on bootstrap methods, a topic I 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.

I believe the techniques and algorithms are still useful although strategy of use may change with the change in processing speed.

8 of 12 people found the following review helpful:

1.0 out of 5 stars Major errors in book, April 11, 2005

By 

jjackk007 (New York City) - See all my reviews

This review is from: Elements of Computational Statistics (Hardcover)

Wait for the 2nd edition before you buy this book, as the 1st (current) edition contains major typographical errors (I hope they're only typos!). See Gentle's LONG errata sheet on his website.

I'm taking an advanced statistical computing course, and sorting out the typos with some of the equations has wasted a significant amount of class and study time. Unforgiveable!

This book never should have made it out of production. I expect the 2nd edition could be worthwhile, the current edition definately is not.