Elements of Large-Sample Theory

Written by one of the main figures in twentieth century statistics, this book provides an introduction to large sample theory at a level suitable for researchers and graduate students in statistics and applied areas.

Author is one of the most important figures in modern statistics as well as a great expositor Unique approach brings a unified treatment to a topic that is usually lost by some at a lower level of statistical proficiency Discusses a broad range of applications, written at an elementary level suitable for non-statisticians in applied fields

Klappentext

Elements of Large-Sample Theory provides a unified treatment of first- order large-sample theory. It discusses a broad range of applications including introductions to density estimation, the bootstrap, and the asymptotics of survey methodology. The book is written at an elementary level and is suitable for students at the master's level in statistics and in aplied fields who have a background of two years of calculus. E.L. Lehmann is Professor of Statistics Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands, and the University of Chicago. Also available: Lehmann/Casella, Theory at Point Estimation, 2nd ed. Springer-Verlag New York, Inc., 1998, ISBN 0- 387-98502-6 Lehmann, Testing Statistical Hypotheses, 2nd ed. Springer-Verlag New York, Inc., 1997, ISBN 0-387-94919-4

Zusammenfassung

From a review:

EUROPEAN MATHEMATICAL SOCIETY

"The book also contains rich collection of problems and a useful list of references, and can be warmly recommended as a complementary text to lectures on mathematical statistics, as well as a textbook for more advanced courses."

Inhalt
Mathematical Background.- Convergence in Probability and in Law.- Performance of Statistical Tests.- Estimation.- Multivariate Extensions.- Nonparametric Estimation.- Efficient Estimators and Tests.