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Jackknife and Bootstrap

Name: Jackknife and Bootstrap
Brand: Springer-Verlag New York Inc.
SKU: 9781461269038
Price: 117347 HUF
Availability: InStock

Szerző: Jun Shao, Dongsheng Tu

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

Küldés 10-18 napon belül

117 347 Ft

The jackknife and bootstrap are the most popular data-resampling meth ods used in statistical analys...

Információk a könyvről

Szerző

Jun Shao, Dongsheng Tu

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2012

oldal

517

EAN

9781461269038

ISBN

1461269032

Enbook ID

06794983

Kiadó

Springer-Verlag New York Inc.

Súly

819

Méretek

155 x 235 x 29

Teljes leírás

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.