An Introduction to the Bootstrap
eBook - PDF

An Introduction to the Bootstrap

  1. 456 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

An Introduction to the Bootstrap

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 Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Chapman and Hall/CRC
Year
1994
Print ISBN
9781032359304
eBook ISBN
9781000064988
Edition
1
Topic
Mathematics
Subtopic
Probability & Statistics
Index
Mathematics

Table of contents

  1. Cover
  2. Series Page
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Contents
  7. Preface
  8. 1 Introduction
  9. 2 The accuracy of a sample mean
  10. 3 Random samples and probabilities
  11. 4 The empirical distribution function and the plug-in principle
  12. 5 Standard errors and estimated standard errors
  13. 6 The bootstrap estimate of standard error
  14. 7 Bootstrap standard errors: some examples
  15. 8 More complicated data structures
  16. 9 Regression models
  17. 10 Estimates of bias
  18. 11 The jackknife
  19. 12 Confidence intervals based on bootstrap “tables”
  20. 13 Confidence intervals based on bootstrap percentiles
  21. 14 Better bootstrap confidence intervals
  22. 15 Permutation tests
  23. 16 Hypothesis testing with the bootstrap
  24. 17 Cross-validation and other estimates of prediction error
  25. 18 Adaptive estimation and calibration
  26. 19 Assessing the error in bootstrap estimates
  27. 20 A geometrical representation for the bootstrap and jackknife
  28. 21 An overview of nonparametric and parametric inference
  29. 22 Further topics in bootstrap confidence intervals
  30. 23 Efficient bootstrap computations
  31. 24 Approximate likelihoods
  32. 25 Bootstrap bioequivalence
  33. 26 Discussion and further topics
  34. Appendix: software for bootstrap computations
  35. References
  36. Author index
  37. Subject index