Princeton University Library Catalog

Mathematical statistics / Jun Shao.

Shao, Jun [Browse]
New York : Springer, ©1999.
1 online resource (xiv, 529 pages).
Springer texts in statistics. [More in this series]
Summary note:
This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph. D. degree in statistics. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are useful in statistics. The second chapter introduces some fundamental concepts in statistical decision theory and inference. Chapters 3-7 contain detailed studies on some important topics: unbiased estimation, parametric estimation, nonparametric estimation, hypothesis testing, and confidence sets. A large number of exercises in each chapter provide not only practice problems for students, but also many additional results. In addition to the classical results that are typically covered in a textbook of a similar level, this book introduces some topics in modern statistical theory that have been developed in recent years, such as Markov chain Monte Carlo, quasi-likelihoods, empirical likelihoods, statistical functionals, generalized estimation equations, the jackknife, and the bootstrap. Jun Shao is Professor of Statistics at the University of Wisconsin, Madison.
Bibliographic references:
Includes bibliographical references (pages 493-503) and indexes.
Source of description:
Print version record.
Cover -- Preface -- Table of Contents -- 1. Probability Theory -- 2. Fundamentals of Statistics -- 3. Unbiased Estimation -- 4. Estimation in Parametric Models -- 5. Estimation in Nonparametric Models -- 6. Hypothesis Tests -- 7. Confidence Sets -- References -- Appendix A -- Abbreviations -- Appendix B -- Notation -- Author Index.
Mathematical statistics [Browse]
Other title(s):
Springer book archives.
  • 0387227598 ((electronic bk.))
  • 9780387227597 ((electronic bk.))
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