Bootstrap Methods and their Application

This book on statistical methods was first published in 1997 and has code on a supporting website.

A. C. Davison (Author), D. V. Hinkley (Author)

9780521574716, Cambridge University Press

Paperback, published 28 October 1997

594 pages, 120 b/w illus. 70 tables 200 exercises
25.1 x 17.8 x 3.3 cm, 1 kg

'The coverage is comprehensive, making the book very useful … The book is well written and is at a level which ensures its usefulness for a wide range of readers.' C. J. Field

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 1997 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.

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.

Subject Areas: Probability & statistics [PBT]