Springer Texts in Statistics - MIM

1 Springer Texts in Statistics Advisors: George Casella Stephen Fienberg Ingram Olkin Springer Texts in Statistics Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics : Theory and Applications Brockwell and Davis: Introduction to Times Series and Forecasting, Second Edition Chow and Teicher: Probability Theory: Independence, Interchangeability, Martingales, Third Edition Christensen: Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data: Nonparametric Regression and Response Surface Maximization, Second Edition Christensen: Log-Linear Models and Logistic Regression, Second Edition Christensen: Plane Answers to Complex Questions: The Theory of Linear Models, Third Edition Creighton: A First Course in Probability Models and Statistical Inference Davis: Statistical Methods for the Analysis of Repeated Measurements Dean and Voss: Design and Analysis of Experiments du Toit, Steyn, and Stumpf.

2 Graphical Exploratory Data Analysis Durrett: Essentials of Stochastic Processes Edwards: Introduction to Graphical Modelling, Second Edition Finkelstein and Levin: Statistics for Lawyers Flury: A First Course in Multivariate Statistics Jobson: Applied Multivariate Data Analysis, Volume I: Regression and Experimental Design Jobson: Applied Multivariate Data Analysis, Volume II: Categorical and Multivariate Methods Kalbfleisch: Probability and Statistical Inference, Volume I: Probability, Second Edition Kalbfleisch: Probability and Statistical Inference, Volume II: Statistical Inference, Second Edition Karr: Probability Keyfitz: Applied Mathematical Demography, Second Edition Kiefer: Introduction to Statistical Inference Kokoska and Nevison: Statistical Tables and Formulae Kulkarni: Modeling, Analysis, Design, and Control of Stochastic Systems Lange: Applied Probability Lehmann: Elements of Large-Sample Theory Lehmann: Testing Statistical Hypotheses, Second Edition Lehmann and Casella: Theory of Point Estimation, Second Edition Lindman.

3 Analysis of Variance in Experimental Design Lindsey: Applying Generalized Linear Models (continued after index). Jun Shao Mathematical Statistics Second Edition Jun Shao Department of Statistics University of Wisconsin, Madison Madison, WI 53706-1685. USA. Editorial Board George Casella Stephen Fienberg Ingram Olkin Department of Statistics Department of Statistics Department of Statistics University of Florida Carnegie Mellon University Stanford University Gainesville, FL 32611-8545 Pittsburgh, PA 15213-3890 Stanford, CA 94305. USA USA USA. With 7 figures. Library of Congress Cataloging-in-Publication Data Shao, Jun.

4 Mathematical Statistics / Jun Shao. 2nd ed. p. cm. ( Springer Texts in Statistics ). Includes bibliographical references and index. ISBN 0-387-95382-5 (alk. paper). 1. Mathematical Statistics . I. Title. II. Series. 2003. dc21 2003045446. ISBN 0-387-95382-5 Printed on acid-free paper. ISBN-13 978-0-387-95382-3. 2003 Springer Science+Business Media, LLC. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher ( Springer Science+Business Media, LLC., 233 Spring St., New York, , 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis.

5 Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America. th 9 8 7 6 5 4 (corrected printing as of 4 printing, 2007). To Guang, Jason, and Annie Preface to the First Edition This book is intended for a course entitled Mathematical Statistics offered at the Department of Statistics , University of Wisconsin-Madison.

6 This course, taught in a mathematically rigorous fashion, covers essential ma- terials in statistical theory that a first or second year graduate student typically needs to learn as preparation for work on a degree in statis- tics. The course is designed for two 15-week semesters, with three lecture hours and two discussion hours in each week. Students in this course are assumed to have a good knowledge of advanced calculus. A course in real analysis or measure theory prior to this course is often recommended. Chapter 1 provides a quick overview of important concepts and results in measure-theoretic probability theory that are used as tools in math- ematical Statistics .

7 Chapter 2 introduces some fundamental concepts in Statistics , including statistical models, the principle of sufficiency in data reduction, and two statistical approaches adopted throughout the book: statistical decision theory and statistical inference. Each of Chapters 3. through 7 provides a detailed study of an important topic in statistical de- cision theory and inference: Chapter 3 introduces the theory of unbiased estimation; Chapter 4 studies theory and methods in point estimation un- der parametric models; Chapter 5 covers point estimation in nonparametric settings; Chapter 6 focuses on hypothesis testing; and Chapter 7 discusses interval estimation and confidence sets.

8 The classical frequentist approach is adopted in this book, although the Bayesian approach is also introduced ( , , , and ). Asymptotic (large sample) theory, a cru- cial part of statistical inference, is studied throughout the book, rather than in a separate chapter. About 85% of the book covers classical results in statistical theory that are typically found in textbooks of a similar level. These materials are in the Statistics Department's qualifying examination syllabus. This part of the book is influenced by several standard textbooks, such as Casella and vii viii Preface to the First Edition Berger (1990), Ferguson (1967), Lehmann (1983, 1986), and Rohatgi (1976).

9 The other 15% of the book covers some topics in modern statistical theory that have been developed in recent years, including robustness of the least squares estimators, Markov chain Monte Carlo, generalized linear models, quasi-likelihoods, empirical likelihoods, statistical functionals, generalized estimation equations, the jackknife, and the bootstrap. In addition to the presentation of fruitful ideas and results, this book emphasizes the use of important tools in establishing theoretical results. Thus, most proofs of theorems, propositions, and lemmas are provided or left as exercises.

10 Some proofs of theorems are omitted (especially in Chapter 1), because the proofs are lengthy or beyond the scope of the book (references are always provided). Each chapter contains a number of examples. Some of them are designed as materials covered in the discussion section of this course, which is typically taught by a teaching assistant (a senior graduate student). The exercises in each chapter form an important part of the book. They provide not only practice problems for students, but also many additional results as complementary materials to the main text.