Mathematical Statistics

ISBN-10: 0387953825
ISBN-13: 9780387953823
Edition: 2nd 2003 (Revised)
Authors: Jun Shao
List price: $109.00 Buy it from $66.68
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Description: 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  More...

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Book details

List price: $109.00
Edition: 2nd
Copyright year: 2003
Publisher: Springer
Publication date: 10/5/2007
Binding: Hardcover
Pages: 592
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 2.508

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 usefulin 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. Also available: Jun Shao and Dongsheng Tu, The Jackknife and Bootstrap, Springer- Verlag New York, Inc., 1995, Cloth, 536 pp., 0-387-94515-6.

Probability Theory
Fundamentals of Statistics
Unbiased Estimation
Estimation in Parametric Models
Estimation in Nonparametric Models
Hypothesis Tests
Confidence Sets

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