An Introduction to the Bootstrap

Front Cover
CRC Press, May 15, 1994 - Mathematics - 456 pages
6 Reviews
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 they need to analyze and understand complicated data sets.
  

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Contents

The accuracy of a sample mean
10
Random samples and probabilities
17
The empirical distribution function and the plugin
31
Standard errors and estimated standard errors
39
The bootstrap estimate of standard error
45
some examples
60
More complicated data structures
86
Regression models
105
Crossvalidation and other estimates of prediction
237
Adaptive estimation and calibration
258
Assessing the error in bootstrap estimates
271
A geometrical representation for the bootstrap
283
An overview of nonparametric and parametric
296
Further topics in bootstrap confidence intervals
321
Efficient bootstrap computations
338
Approximate likelihoods
358

Estimates of bias
124
The jackknife
141
Confidence intervals based on bootstrap tables
153
Confidence intervals based on bootstrap
168
Better bootstrap confidence intervals
178
Permutation tests
202
Hypothesis testing with the bootstrap
220
Bootstrap bioequivalence
372
Discussion and further topics
392
software for bootstrap computations
398
References
413
Author index
426
Copyright

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About the author (1994)

Bradley Efron is Max H. Stein Professor of Statistics and Biostatistics at the Stanford University School of Humanities and Sciences, and the Department of Health Research and Policy with the School of Medicine.

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