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Testing Statistical Hypotheses (Springer Texts in Statistics) [Hardcover]

Erich L. Lehmann , Joseph P. Romano

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Book Description

10 Sep 2008 0387988645 978-0387988641 3rd ed. 2005. Corr. 2nd printing 2008
The third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760.


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Review

From the reviews of the third edition: "This new edition of the classic and fundamental text on the theory of testing hypotheses is an essential addition to the bookshelf of mathematical statisticians." Short Book Reviews of the International Statistical Institute,  December 2005 "What I like much about this book is its illustrative language and the numerous examples that make it easier to understand the complex matter presented. The comprehensible notation and the excellent structure further add to the readability of this book."Biometrics, March 2006 "The third edition of TSH retains much of the same focus as the second edition...The quality of the new material alone justifies the publication of a third edition to a book already well suited. As readers of the earlier editions have come to expect, TSH contains an enormous number of examples, problems, and ideas. The writing and presentation are excellent." Journal of the American Statistical Association, June 2006 "This is the third edition of a famous book which was first published in 1959. The first rigorous exposition to the theory of testing for any student of statistics has been invariably through this masterpiece. … Needless to say, this book continues to be the benchmark in the rigorous treatment of testing of hypothesis. The new chapters on the asymptotic behaviour of most of the popular tests is a true value addition." (Arup Bose, Sankhya, Vol. 67 (4), 2005) "This is a revised and expanded version of the well-known second edition from 1986 … . The exposition is clear and sufficiently rigorous. … With this edition ‘Testing Statistical Hypothesis’ will undoubtedly continue to be the standard graduate level textbook on statistical testing." (R. Schlittgen, Zentralblatt MATH, Vol. 1076, 2006) "This monograph under review is the third edition … of Erich L. Lehmann’s classical graduate text on ‘Testing statistical hypotheses’. … the second edition from 1986 has comprehensively been reorganized … . Additional insight into the historical background and recent developments is given … . More than 1,000 original references are provided. … an excellent and demanding treatment of modern statistical test theory. There is no doubt that it remains and will even more be used as a standard monograph … ." (J. Steinebach, Metrika, Vol. 64, 2006)

From the Back Cover

The third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760. 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. He is the author of Elements of Large-Sample Theory and (with George Casella) he is also the author of Theory of Point Estimation, Second Edition. Joseph P. Romano is Professor of Statistics at Stanford University. He is a recipient of a Presidential Young Investigator Award and a Fellow of the Institute of Mathematical Statistics. He has coauthored two other books, Subsampling with Dimitris Politis and Michael Wolf, and Counterexamples in Probability and Statistics with Andrew Siegel.

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First Sentence
The raw material of a statistical investigation is a set of observations; these are the values taken on by random variables X whose distribution P is at least partly unknown. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index
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Customer Reviews

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Amazon.com: 4.0 out of 5 stars  6 reviews
36 of 38 people found the following review helpful
5.0 out of 5 stars classic text with new publisher 13 Feb 2008
By Michael R. Chernick - Published on Amazon.com
Format:Hardcover
This text was commonly used as a graduate text in mathematical statistics in the 1970s when I was a graduate student at Stanford University. It was the best and most detailed text on the theory of hypothesis testing. Over the years it remained so and twenty years after publication, when it was outdated by research advances it was revised by Professor Lehmann. The second edition originally published by Wiley went out of print but has now been reprinted by Springer-Verlag. This is a great book for any statistician to have on his bookshelf, a must have reference!
5 of 6 people found the following review helpful
5.0 out of 5 stars 3rd edition has lots of new material 11 Feb 2008
By R. D. Rivers - Published on Amazon.com
Format:Hardcover
The 3rd edition has an entirely new set of chapters covering asymptotics. I found this to be a very readable survey, including a good discussion of local asymptotic normality, which is not treated in more elementary texts. There's some overlap between this book and Lehman's Theory of Point Estimation. It's not obvious which should be read first, but both books are very well written with many interesting problems.
7 of 10 people found the following review helpful
3.0 out of 5 stars A few details are not quite right 24 Oct 2009
By Harold M. Kaplan - Published on Amazon.com
Format:Hardcover|Amazon Verified Purchase
Lehmann and Romano is a wonderful, beautiful, necessary book for the shelf of every serious statistician, but in a few ways it is not quite right. Some important topics are omitted. At least one important topic is much more important than the book says. At least one statement, while correct, may be read incorrectly by beginners. At least one proof is unreadable.

An omission is heteroskedasticity. The usual tests for 2-samples and k-samples are wrong in its presence. The same is true for the usual test for blocks and treatments. However, for all these there do exist tests which are conservative in the presence of heteroskedasticity. For 2-samples and for two treatments there are exact tests. Another omission is Doob's inequality for nonnegative martingales, which connects up some Bayes tests with some frequentist tests.

Simpson's paradox (page 132 bottom) is treated at length in the book, but the treatment does not suffice, and there might not be any treatment which could suffice. The paradox strikes at nearly all of what statisticians do. The book ought to use big bold-face type for the statement of the paradox. Also, the book ought to include an example, not just give a reference.

The account of Monte Carlo tests (page 442) may seem to suggest that Monte Carlo gives only an approximation and that its accuracy depends on how many random numbers are used. The reader is not told that Monte Carlo tests are commonly exact tests for small samples. (And where in the book is the word "exact"?)

On page 353 I am entirely unable to follow the (very short) proof of Theorem 9.1.3. The complexity of the notation is perhaps responsible.
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