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- 456 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
An Introduction to the Bootstrap
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About This Book
An Introduction to the Bootstrap arms scientists and engineers as well as statisticians with the computational techniques they need to analyze and understand complicated data sets. The bootstrap is a computer-based method of statistical inference that answers statistical questions without formulas and gives a direct appreciation of variance, bias, coverage, and other probabilistic phenomena. This book presents an overview of the bootstrap and related methods for assessing statistical accuracy, concentrating on the ideas rather than their mathematical justification. Not just for beginners, the presentation starts off slowly, but builds in both scope and depth to ideas that are quite sophisticated.
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Yes, you can access An Introduction to the Bootstrap by Bradley Efron, R.J. Tibshirani in PDF and/or ePUB format, as well as other popular books in Mathématiques & Probabilités et statistiques. We have over one million books available in our catalogue for you to explore.
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Table of contents
- Cover
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- Preface
- 1 Introduction
- 2 The accuracy of a sample mean
- 3 Random samples and probabilities
- 4 The empirical distribution function and the plug-in principle
- 5 Standard errors and estimated standard errors
- 6 The bootstrap estimate of standard error
- 7 Bootstrap standard errors: some examples
- 8 More complicated data structures
- 9 Regression models
- 10 Estimates of bias
- 11 The jackknife
- 12 Confidence intervals based on bootstrap “tables”
- 13 Confidence intervals based on bootstrap percentiles
- 14 Better bootstrap confidence intervals
- 15 Permutation tests
- 16 Hypothesis testing with the bootstrap
- 17 Cross-validation and other estimates of predictior error
- 18 Adaptive estimation and calibration
- 19 Assessing the error in bootstrap estimates
- 20 A geometrical representation for the bootstrap and jackknife
- 21 An overview of nonparametric and parametric inference
- 22 Further topics in bootstrap confidence intervals
- 23 Efficient bootstrap computations
- 24 Approximate likelihoods
- 25 Bootstrap bioequivalence
- 26 Discussion and further topics
- Appendix: software for bootstrap computations
- References
- Author index
- Subject index