
Elements of Large-Sample Theory
E.L. Lehmann(Author)
Springer (Publisher)
Published on 19. November 2010
Book
Paperback/Softback
XII, 632 pages
978-1-4419-3136-8 (ISBN)
Description
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.
Reviews / Votes
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."
More details
Series
Edition
Softcover reprint of the original 1st ed. 1999
Language
English
Place of publication
New York
United States
Target group
Primary & secondary/elementary & high school
Graduate
Illustrations
XII, 632 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 35 mm
Weight
966 gr
ISBN-13
978-1-4419-3136-8 (9781441931368)
DOI
10.1007/b98855
Schweitzer Classification
Other editions
Additional editions

Person
E.L. Lehmann (1917 - 2009) was an American statistician and professor of statistics at the University of California, Berkeley. He made significant contributions to nonparametric hypothesis testing, and he is one of the eponyms of the Lehmann-Scheffé theorem and of the Hodges-Lehmann estimator. Dr. Lehmann was 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. He was the author of Elements of Large-Sample Theory (Springer 1999) and Theory of Point Estimation, Second Edition (Springer 1998, with George Casella).
Joseph P. Romano has been on faculty in the Statistics Department at Stanford since 1986. Since 2007, he has held a joint professorship appointment in both Statistics and Economics. He is a coauthor of three books, as well as over 100 journal articles. Dr. Romano was named NOGLSTP's 2021 LGBTQ+ Scientist of the Year, has been a recipient of the Presidential Young Investigator Award and many other grants from the National Science Foundation, and is a Fellow of the Institute of Mathematical Statistics and of the International Association of Applied Econometrics. His research has focused on such topics as: bootstrap and resampling methods, subsampling, randomization methods, inference, optimality, large-sample theory, nonparametrics, multiple hypothesis testing, and econometrics. He has invented or co-invented a variety of new statistical methods, including subsampling and the stationary bootstrap, as well as methods for multiple hypothesis testing. These methods have been applied to such diverse fields as clinical trials, climate change, finance, and economics.
Content
Mathematical Background.- Convergence in Probability and in Law.- Performance of Statistical Tests.- Estimation.- Multivariate Extensions.- Nonparametric Estimation.- Efficient Estimators and Tests.