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Bootstrap Methods and their Application (Davison)

 
 Author(s)  A. C. Davison, D. V. Hinkley
 Title  Bootstrap Methods and their Application
 Edition  
 Year  1997
 Publisher  Cambridge University Press
 ISBN  978-0-521-57471-4
 Website  www.cambridge.org
 http://statwww.epfl.ch/davison/BMA/  author's book site
 

Data sets and S code (also R code) library functions for bootstrap methods arre available at the author's book site (http://statwww.epfl.ch/davison/BMA/)


From the author's website:
  • A library of S-Plus routines to accompany the book has been written by Angelo Canty and is available free of charge.
  • These routines form part of the base distribution of the statistical environment R.  To access them, simply download and install R, and type library(boot).



Description

Bootstrap methods are computer-intensive methods of statistical analysis, which use simulation to calculate standard errors, confidence intervals, and significance tests.

The methods apply for any level of modelling, and so can be used for fully parametric, semiparametric, and completely nonparametric analysis.

This book gives a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis.

Applications include stratified data; finite populations; censored and missing data; linear, nonlinear, and smooth regression models; classification; time series and spatial problems.

Special features of the book include: extensive discussion of significance tests and confidence intervals; material on various diagnostic methods; and methods for efficient computation, including improved Monte Carlo simulation.

Each chapter includes both practical and theoretical exercises.

S-Plus programs for implementing the methods described in the text are available from the supporting website.



Table of Contents

1. Introduction
2. The basic bootstraps
3. Further ideas
4. Tests
5. Confidence intervals
6. Regression models
7. Further topics in regression
8. Complex dependence
9. Improved calculation
10. Semiparametric likelihood inference
11. Computer implementation

Appendix
Cumulant calculations
Bibliography
Index






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