Mathematical statistics

Jun Shao - Collection Springer texts in statistics

592 pages, parution le 15/01/2003 (2^eme édition)

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Résumé

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.

Written for: Graduate students

Sommaire

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

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	PAPIER
Éditeur(s)	Springer
Auteur(s)	Jun Shao
Collection	Springer texts in statistics
Parution	15/01/2003
Édition	2^eme édition
Nb. de pages	592
Format	16 x 24
Couverture	Relié
Poids	1047g
Intérieur	Noir et Blanc
EAN13	9780387953823
ISBN13	978-0-387-95382-3