The Jackknife and Bootstrap. Springer Series in Statistics

Shao, Jun and Dongsheng Tu:

ISBN 10: 0387945156 / ISBN 13: 9780387945156

Published by Springer, 1995

About this Item

516 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 900.

Seller Inventory # 2289235

Bibliographic Details

• Title: The Jackknife and Bootstrap. Springer Series in Statistics
• Publisher: Springer
• Publication Date: 1995
• Language: English
• ISBN 10: 0387945156
• ISBN 13: 9780387945156
• Binding: gebundene Ausgabe
• Edition: 2nd Edition
• Condition: Gut
• Weight: 900 grams
• Seller catalogs:
  • 90 Varia

About this title

Synopsis

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.