An Introduction to the Bootstrap

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CRC Press, 15 mai 1994 - 456 pages
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques t
 

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Table des matières

1 Introduction
1
2 The accuracy of a sample mean
10
3 Random samples and probabilities
17
4 The empirical distribution function and the plugin principle
31
5 Standard errors and estimated standard errors
39
6 The bootstrap estimate of standard error
45
some examples
60
8 More complicated data structures
86
17 Crossvalidation and other estimates of prediction error
237
18 Adaptive estimation and calibration
258
19 Assessing the error in bootstrap estimates
271
20 A geometrical representation for the bootstrap and jackknife
283
21 An overview of nonparametric and parametric inference
296
22 Further topics in bootstrap confidence intervals
321
23 Efficient bootstrap computations
338
24 Approximate likelihoods
358

9 Regression models
105
10 Estimates of bias
124
11 The jackknife
141
12 Confidence intervals based on bootstrap tables
153
13 Confidence intervals based on bootstrap percentiles
168
14 Better bootstrap confidence intervals
178
15 Permutation tests
202
16 Hypothesis testing with the bootstrap
220
25 Bootstrap bioequivalence
372
26 Discussion and further topics
392
software for bootstrap computations
398
References
413
Author index
426
Subject index
430
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