Introduction to the Bootstrap
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Description: 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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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $139.95
Copyright year: 1994
Publisher: CRC Press LLC
Publication date: 5/15/1994
Size: 6.00" wide x 9.00" long x 1.25" tall
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.
|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|
|Estimates of Bias|
|Confidence Intervals Based on Bootstrap "Tables"|
|Confidence Intervals Based on Bootstrap Percentiles|
|Better Bootstrap Confidence Intervals|
|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|
|Discussion and Further Topics|
|Appendix: Software for Bootstrap Computations|