Synopses & Reviews
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. Included with the book is a disk of purpose-written S-Plus programs for implementing the methods described in the text. Computer algorithms are clearly described, and computer code is included on a 3-inch, 1.4M disk for use with IBM computers and compatible machines. Users must have the S-Plus computer application.
Review
"...well-illustrated examples..." Sociological Methods and Research"The number and diversity of examples greatly enhance the understanding of the text. We marvel at the number of resamples that were taken in support of the book! The authors use hundreds of plots and dozens of tables to demonstrate and evaluate the uses of bootstrap... Statisticians with little or no familiarity with the bootstrap will find Bootstrap Methods and Their Application to be a thorough introduction to its use in solving real-world problems...We recommend this book most highly. It made us stop and think regularly and contributed tremendously to our understanding of the bootstrap. It is an excellent book for professors, students, practicioners, and researchers alike." Thomas Loughin and Christopher R. Bilder, Journal of the American Statistical Association"...a comprehensive and extremely readable overview of the current state of art in bootstrap methodology. Through the numerous exercises, practicals and examples the reader obtains a good understanding for the strength of bootstrap methods, the problems for which they work and how to avoid their pitfalls. I strongly recommend this book to anybody who uses, or wishes to use, bootstrap methods...this book should be part of your library." The University of Adelaide"Statisticians with little or no familiarity with the bootstrap will find ootstrap Methods and Their Application^ to be a thorough introduction to its use in solving real-world problems...We recommend this book most highly. It made us stop and think regularly and contributed tremendously to our understanding of the bootstrap. It is an excellent book for professors, students, practitioners, and researchers alike." Kansas State University"The authors have done an excellent job of mixing up the theory and the applications of bootstrap...Every applied statistician who wants to apply bootstrap with some knowledge of the underlined theory so that it is not applied improperly should take a look at this book." Technometrics
Synopsis
Statistical methods book, including programs on disk.
Synopsis
Bootstrap methods enable fairly sophisticated statistical calculations to be done by computer simulation. The range of application is broad: from biology and medicine through to econometrics and finance. Compared with other treatments, applications are thoroughly covered here, with an emphasis on practical implementation (computer algorithms are clearly described, and computer code is available on the supporting website).
Synopsis
The recently-developed Bootstrap methods enable fairly sophisticated statistical calculations to be done by computer simulation. This frees the application of both elementary and advanced statistical methods from unreliable mathematical formulae, and makes them more generally applicable. The range of application is very wide; from epidemiology, biology and medicine through to econometrics and finance. In comparison with other books on the subject, here this variety of applications is covered much more thoroughly with a strong emphasis on practical implementation (computer algorithms are clearly described, and computer code is available on 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.