Mathematical statistics /
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 chapte...
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| Format: | eBook |
| Language: | English |
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New York :
Springer,
[1999]
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| Series: | Springer texts in statistics.
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| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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. |
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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (xiv, 529 pages) |
| Bibliography: | Includes bibliographical references (pages 493-503) and indexes. |
| ISBN: | 0387227598 (electronic bk.) 9780387227597 (electronic bk.) |