UID:
almafu_9959235945602883
Format:
1 online resource (x, 582 pages) :
,
digital, PDF file(s).
ISBN:
1-107-26381-6
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1-107-26638-6
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1-107-26326-3
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1-107-26789-7
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1-107-26434-0
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1-107-26682-3
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1-107-26990-3
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0-511-80284-6
Series Statement:
Cambridge series on statistical and probabilistic mathematics ; 1
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
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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
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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
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3.4 Smooth Estimates of F3.5 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
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4.2.3 Parametric bootstrap tests4.2.4 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
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5.4 Theoretical Comparison of Methods5.4.1 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
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6.2.6 Non-constant variance: weighted error resampling
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English
Additional Edition:
ISBN 0-521-57471-4
Additional Edition:
ISBN 0-521-57391-2
Language:
English
URL:
https://doi.org/10.1017/CBO9780511802843
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