1st Edition

An Introduction to the Bootstrap

By Bradley Efron, R.J. Tibshirani Copyright 1994
    456 Pages
    by Chapman & Hall

    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.

    The Accuracy of a Sample Mean
    Random Samples and Probabilities
    The Empirical Distribution Function and the Plug-In Principle
    Standard Errors and Estimated Standard Errors
    The Bootstrap Estimate of Standard Error
    Bootstrap Standard Errors: Some Examples
    More Complicated Data Structures
    Regression Models
    Estimates of Bias
    The Jackknife
    Confidence Intervals Based on Bootstrap "Tables"
    Confidence Intervals Based on Bootstrap Percentiles
    Better Bootstrap Confidence Intervals
    Permutation Tests
    Hypothesis Testing with the Bootstrap
    Cross-Validation and Other Estimates of Prediction Error
    Adaptive Estimation and Calibration
    Assessing the Error in Bootstrap Estimates
    A Geometrical Representation for the Bootstrap and Jackknife
    An Overview of Nonparametric and Parametric Inference
    Further Topics in Bootstrap Confidence Intervals
    Efficient Bootstrap Computations
    Approximate Likelihoods
    Bootstrap Bioequivalence
    Discussion and Further Topics
    Appendix: Software for Bootstrap Computations


    Bradley Efron, Department of Statistics Stanford University and Robert J. Tibshirani, Department of Preventative Medicine and Biostatistics and Department of Statistics, University of Toronto.

    "...an excellent book, and worth a reading by most students and practitioners in statistics... Throughout the book, the authors have spent a lot of effort in introducing difficult ideas in a simple, easy-to-understand manner..."
    - Hong Kong Statistical Society Newsletter

    "... written in a style that makes difficult statistical concepts easy to understand ...a wonderful text for the engineer who would like to apply and understand the many different bootstrap techniques that have appeared in the literature in the last fifteen years. It makes an excellent reference text that should grace the shelves of both statisticians and non-statisticians."
    - Journal of Quality Technology