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Bootstrap methods and their application

Author: A C Davison; D V Hinkley
Publisher: Cambridge, UK ; New York : Cambridge University Press, 1997. ©1997
Series: Cambridge series on statistical and probabilistic mathematics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book on statistical methods was first published in 1997 and has code on a supporting website.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Davison, A. C. (Anthony Christopher).
Bootstrap methods and their application.
Cambridge ; New York, NY, USA : Cambridge University Press, 1997
(DLC) 96030064
(OCoLC)36130645
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: A C Davison; D V Hinkley
ISBN: 9781107269903 1107269903 9781107263260 1107263263 9780511802843 0511802846
OCLC Number: 880530770
Notes: 6.2.6 Non-constant variance: weighted error resampling.
Description: 1 online resource (x, 582 pages) : illustrations.
Contents: Cover; Half-title; Title; Copyright; Contents; Preface; 1 Introduction; 2 The Basic Bootstraps; 2.1 Introduction; 2.1.1 Statistical functions; 2.1.2 Objectives; 2.2 Parametric Simulation; 2.2.1 Moment estimates; 2.2.2 Distribution and quantile estimates; 2.3 Nonparametric Simulation; 2.3.1 Comparison with parametric methods; 2.3.2 Effects of discreteness; 2.4 Simple Confidence Intervals; 2.5 Reducing Error; 2.5.1 Statistical error; 2.5.2 Simulation error; 2.6 Statistical Issues; 2.6.1 When does the bootstrap work?; 2.6.2 Rough statistics: unsmooth and unstable; 2.6.3 Conditional properties. 2.6.4 When might the bootstrap fail? 2.7 Nonparametric Approximations for Variance and Bias; 2.7.1 Delta methods; 2.7.2 Influence function and nonparametric delta method; 2.7.3 Jackknife estimates; 2.7.4 Empirical influence values via regression; 2.7.5 Variance estimates; 2.8 Subsampling Methods; 2.8.1 Jackknife methods; 2.8.2 All-subsamples method; 2.8.3 Half-sampling methods; 2.9 Bibliographic Notes; 2.10 Problems; 2.11 Practicals; 3 Further Ideas; 3.1 Introduction; 3.2 Several Samples; 3.2.1 Influence functions and variance approximations; 3.3 Semiparametric Models. 3.4 Smooth Estimates of F 3.5 Censoring; 3.5.1 Censored data; 3.5.2 Resampling plans; 3.6 Missing Data; 3.7 Finite Population Sampling; 3.8 Hierarchical Data; 3.9 Bootstrapping the Bootstrap; 3.9.1 Bias correction of bootstrap calculations; 3.9.2 Variation of properties of T; 3.10 Bootstrap Diagnostics; 3.10.1 Jackknife-after-bootstrap; 3.10.2 Linearity; 3.11 Choice of Estimator from the Data; 3.12 Bibliographic Notes; 3.13 Problems; 3.14 Practicals; 4 Tests; 4.1 Introduction; 4.2 Resampling for Parametric Tests; 4.2.1 Monte Carlo tests; 4.2.2 Markov chain Monte Carlo tests. 4.2.3 Parametric bootstrap tests 4.2.4 Graphical tests; 4.2.5 Choice of R; 4.3 Nonparametric Permutation Tests; 4.4 Nonparametric Bootstrap Tests; 4.4.1 Studentized bootstrap method; 4.4.2 Conditional bootstrap tests; 4.4.3 Multiple testing; 4.5 Adjusted P-values; 4.6 Estimating Properties of Tests; 4.7 Bibliographic Notes; 4.8 Problems; 4.9 Practicals; 5 Confidence Intervals; 5.1 Introduction; 5.2 Basic Confidence Limit Methods; 5.2.1 Parametric models; 5.2.2 Nonparametric models; 5.2.3 Choice of R; 5.3 Percentile Methods; 5.3.1 Basic percentile method; 5.3.2 Adjusted percentile method. 5.4 Theoretical Comparison of Methods 5.4.1 Second-order accuracy; 5.4.2 The ABC method; 5.5 Inversion of Significance Tests; 5.6 Double Bootstrap Methods; 5.7 Empirical Comparison of Bootstrap Methods; 5.8 Multiparameter Methods; 5.9 Conditional Confidence Regions; 5.10 Prediction; 5.11 Bibliographic Notes; 5.12 Problems; 5.13 Practicals; 6 Linear Regression; 6.1 Introduction; 6.2 Least Squares Linear Regression; 6.2.1 Regression fit and residuals; 6.2.2 Alternative models; 6.2.3 Resampling errors; 6.2.4 Resampling cases; 6.2.5 Significance tests for slope.
Series Title: Cambridge series on statistical and probabilistic mathematics.
Responsibility: A.C. Davison, D.V. Hinkley.
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Abstract:

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  Read more...

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'... 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 Read more...

 
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