Bootstrap methods and their application / A.C. Davison, D.V. Hinkley.

Call Number	519.5/44
Author	Davison, A. C. author.
Title	Bootstrap methods and their application / A.C. Davison, D.V. Hinkley. Bootstrap Methods & their Application
Physical Description	1 online resource (x, 582 pages) : digital, PDF file(s).
Series	Cambridge series on statistical and probabilistic mathematics ; 1
Notes	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Summary	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.
Added Author	Hinkley, D. V., author.
Subject	Bootstrap (Statistics)
Multimedia	https://doi.org/10.1017/CBO9780511802843

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Summary	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.
Notes	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject	Bootstrap (Statistics)
Multimedia	https://doi.org/10.1017/CBO9780511802843