Bootstrap Methods and Their Application

Front Cover
Cambridge University Press, Oct 28, 1997 - Computers - 582 pages
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. Author resource page: http://statwww.epfl.ch/davison/BMA/
 

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Contents

Introduction
ix
The Basic Bootstraps
9
22 Parametric Simulation
13
23 Nonparametric Simulation
20
24 Simple Confidence Intervals
25
25 Reducing Error
29
26 Statistical Issues
35
27 Nonparametric Approximations for Variance and Bias
43
64 Aggregate Prediction Error and Variable Selection
288
65 Robust Regression
305
66 Bibliographic Notes
313
67 Problems
314
68 Practicals
319
Further Topics in Regression
324
72 Generalized Linear Models
325
73 Survival Data
344

28 Subsampling Methods
53
29 Bibliographic Notes
57
210 Problems
58
211 Practicals
64
Further Ideas
68
32 Several Samples
69
33 Semiparametric Models
75
34 Smooth Estimates of F
77
35 Censoring
80
36 Missing Data
86
37 Finite Population Sampling
90
38 Hierarchical Data
98
39 Bootstrapping the Bootstrap
101
310 Bootstrap Diagnostics
111
311 Choice of Estimator from the Data
118
312 Bibliographic Notes
121
313 Problems
124
314 Practicals
129
Tests
134
42 Resampling for Parametric Tests
138
43 Nonparametric Permutation Tests
154
44 Nonparametric Bootstrap Tests
159
45 Adjusted Pvalues
173
46 Estimating Properties of Tests
178
47 Bibliographic Notes
181
48 Problems
182
49 Practicals
185
Confidence Intervals
189
52 Basic Confidence Limit Methods
191
53 Percentile Methods
200
54 Theoretical Comparison of Methods
209
55 Inversion of Significance Tests
218
56 Double Bootstrap Methods
221
57 Empirical Comparison of Bootstrap Methods
228
58 Multiparameter Methods
229
59 Conditional Confidence Regions
236
510 Prediction
241
511 Bibliographic Notes
244
512 Problems
245
513 Practicals
249
Linear Regression
254
62 Least Squares Linear Regression
255
63 Multiple Linear Regression
271
74 Other Nonlinear Models
351
75 Misclassification Error
356
76 Nonparametric Regression
360
77 Bibliographic Notes
372
78 Problems
374
79 Practicals
376
Complex Dependence
383
83 Point Processes
411
84 Bibliographic Notes
422
85 Problems
424
86 Practicals
428
Improved Calculation
433
92 Balanced Bootstraps
434
93 Control Methods
442
94 Importance Resampling
446
95 Saddlepoint Approximation
460
96 Bibliographic Notes
479
97 Problems
480
98 Practicals
486
Semiparametric Likelihood Inference
491
102 MultinomialBased Likelihoods
492
103 Bootstrap Likelihood
499
104 Likelihood Based on Confidence Sets
501
105 Bayesian Bootstraps
504
106 Bibliographic Notes
506
107 Problems
508
108 Practicals
511
Computer Implementation
514
112 Basic Bootstraps
517
113 Further Ideas
523
114 Tests
526
115 Confidence Intervals
528
116 Linear Regression
529
117 Further Topics in Regression
532
118 Time Series
535
119 Improved Simulation
537
1110 Semiparametric Likelihoods
541
Cumulant Calculations
543
Bibliography
547
Name Index
560
Example index
562
Subject index
565
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