An exploration of the many different bootstrap techniques. It discusses useful statistical techniques through real data examples and covers nonparametric regression, density estimation, classification trees, and least median squares regression. There are numerous exercises that provide hands-on experience in applying the concepts, and there are descriptions of a number of different computer programs for the methods discussed.
An exploration of the many different bootstrap techniques. It discusses useful statistical techniques through real data examples and covers nonparametric regression, density estimation, classification trees, and least median squares regression. There are numerous exercises that provide hands-on experience in applying the concepts, and there are descriptions of a number of different computer programs for the methods discussed.
Preface 1 Introduction 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 prediction 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
Springer Book Archives
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
|
Ask a Question About this Product More... |
|
