Bootstrap Methods and their Application (Cambridge Series in Statistical and Probabilistic Mathematics)

Críticas:

'... an extremely readable book. I would have no hestitation in recommending it as the most useful reference available for people wishing to learn or teach this subject. Certainly, this book is an essential addition to any library for those who use, or wish to use, bootstrap methodology.' Stephen Brooks, Royal Statistical Society Bulletin

'This book gives a broad and up-to-date coverage of bootstrap methods with numerous applied examples, together with the underlying general concepts developed in a coherent way.' L'Enseignment Mathématique

'Davison and Hinkley's book covers a remarkably broad range of bootstrap topics ... The authors have put considerable thought and time into their exposition. For learning how and when to bootstrap, there is no better start. Metrika

' ... this is a timely, comprehensive and well presented text on the bootstrap, which I recommend to statistical practitioners, researchers and students alike.' James Carpenter, London School of Hygiene and Tropical Medicine

' ... the authors provide a comprehensive and extremely readable overview of the current state of art in bootstrap methodology ... I strongly recommend this book ... this book should be part of your library.' Berwin A. Turlach, Journal of Applied Statistics

'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.' Journal of American Statistical Association

'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

Reseña del editor:

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.

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