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Elements of Large Sample Theory provides a unified treatment of firstorder large-sample theory. It discusses a broad range of applications including introductions to density estimation, the bootstrap, and the asymptotics of survey methodology written at an elementary level. The book 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.
List of contents
Mathematical Background.- Convergence in Probability and in Law.- Performance of Statistical Tests.- Estimation.- Multivariate Extensions.- Nonparametric Estimation.- Efficient Estimators and Tests.
About the author
Erich 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.
Summary
Written by one of the main figures in twentieth century statistics, this book provides a unified treatment of first-order large-sample theory. The book is written at an elementary level making it accessible to most readers.
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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."
