Mathematical Statistics (Hardcover, 2nd ed. 2003. Corr. 4th printing 2007)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Springer Texts in Statistics
Release date:	October 2007
First published:	2003
Authors:	Jun Shao
Dimensions:	235 x 155 x 38mm (L x W x T)
Format:	Hardcover
Pages:	592
Edition:	2nd ed. 2003. Corr. 4th printing 2007
ISBN-13:	978-0-387-95382-3
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	0-387-95382-5
Barcode:	9780387953823

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