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The Jackknife and Bootstrap (Springer Series in Statistics), by Jun Shao, Dongsheng Tu
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The jackknife and bootstrap are the most popular data-resampling meth ods used in statistical analysis. The resampling methods replace theoreti cal derivations required in applying traditional methods (such as substitu tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples. Because of the availability of inexpensive and fast computing, these computer-intensive methods have caught on very rapidly in recent years and are particularly appreciated by applied statisticians. The primary aims of this book are (1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians: practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further devel opments of the resampling methods. The theoretical properties of the jackknife and bootstrap methods are studied in this book in an asymptotic framework. Theorems are illustrated by examples. Finite sample properties of the jackknife and bootstrap are mostly investigated by examples and/or empirical simulation studies. In addition to the theory for the jackknife and bootstrap methods in problems with independent and identically distributed (Li.d.) data, we try to cover, as much as we can, the applications of the jackknife and bootstrap in various complicated non-Li.d. data problems.
- Sales Rank: #6929234 in Books
- Brand: Brand: Springer
- Published on: 1995-07-21
- Released on: 1995-07-21
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.22" w x 6.10" l, 1.65 pounds
- Binding: Paperback
- 517 pages
- Used Book in Good Condition
Most helpful customer reviews
35 of 35 people found the following review helpful.
excellent in theory and simulation results
By Michael R. Chernick
After giving a short course on resampling just in 2001 I had a chance to look through this book a little more carefully. I did not realize how many practical simulations studies the authors have summarized. The research they summarize is not available in any other book (with the exception of classification error rate estimation which is covered in my book and in McLachlan's).
Their discussion at the end of each chapter is helpful. I still think that most practitioners will have difficulty with the theory but the simulation results can be used to provide guidelines for many important problems. Also they develop the theory to many of the more complicated problems. This is something I had not realized earlier.
It is another one of several books on bootstrap that came out in the 1990s. The authors provide a systematic and theoretical treatment of the jackknife and the bootstrap in a variety of contexts including confidence intervals, survey sampling, linear and non-linear models, multivariate and nonparametric settings and time series and other dependent situations.
The book is comprehensive, well-written and contains an extensive list of references. It is advanced and presupposes a fair amount of knowledge of mathematical statistics. It is a very useful reference for statistician, particularly ressearch statisticians. The authors state that their primary aims are "(1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians; practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further developments of the resampling methods."
The authors certainly achieve (1) and (3) but I think the book is at too high a level to reach most applied statisticians and so although they sometimes provide good practical advice based on the theory, most applied statisticians will not have the patience to wade through the theory to get to the advice.
I do not necessarily consider use of resampling methods in situations where there is no theoretical confirmation as misuse. Often applications come ahead of theory and it is worthwhile to try out new techniques even when good theory is lacking. Often, the usefulness of a method can be confirmed by simulation even when asymptotic theory is not available. Also asymptotic theory may be of no value (or could even be misleading) in practical small sample size situations.
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