Bootstrapping : a nonparametric approach to statistical inference
著者
書誌事項
Bootstrapping : a nonparametric approach to statistical inference
（Sage university papers series, . Quantitative applications in the social sciences ; no. 07-095）
Sage Publications, c1993
大学図書館所蔵 件 / 全49件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Bibliography: p. 68-72
内容説明・目次
内容説明
This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . . --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.
目次
PART ONE: INTRODUCTION
Traditional Parametric Statistical Inference
Bootstrap Statistical Inference
Bootstrapping a Regression Model
Theoretical Justification
The Jackknife
Monte Carlo Evaluation of the Bootstrap
PART TWO: STATISTICAL INFERENCE USING THE BOOTSTRAP
Bias Estimation
Bootstrap Confidence Intervals
PART THREE: APPLICATIONS OF BOOTSTRAP CONFIDENCE INTERVALS
Confidence Intervals for Statistics With Unknown Sampling Distributions
Inference When Traditional Distributional Assumptions Are Violated
PART FOUR: CONCLUSION
Future Work
Limitations of the Bootstrap
Concluding Remarks
「Nielsen BookData」 より