Testing Statistical Hypotheses / Edition 3

Testing Statistical Hypotheses / Edition 3

ISBN-10:
1441931783
ISBN-13:
9781441931788
Pub. Date:
11/19/2010
Publisher:
Springer New York
ISBN-10:
1441931783
ISBN-13:
9781441931788
Pub. Date:
11/19/2010
Publisher:
Springer New York
Testing Statistical Hypotheses / Edition 3

Testing Statistical Hypotheses / Edition 3

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Overview

This classic textbook, now available from Springer, summarizes developments in the field of hypotheses testing. Optimality considerations continue to provide the organizing principle. However, they are now tempered by a much stronger emphasis on the robustness properties of the resulting procedures. This book is an essential reference for any graduate student in statistics.


Product Details

ISBN-13: 9781441931788
Publisher: Springer New York
Publication date: 11/19/2010
Series: Springer Texts in Statistics Series
Edition description: Softcover reprint of hardcover 3rd ed. 2005
Pages: 786
Product dimensions: 6.10(w) x 9.20(h) x 1.80(d)

About the Author

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.

Table of Contents

The General Decision Problem.- The Probability Background.- Uniformly Most Powerful Tests.- Unbiasedness: Theory and First Applications.- Unbiasedness: Applications to Normal Distributions.- Invariance.- Linear Hypotheses.- The Minimax Principle.- Multiple Testing and Simultaneous Inference.- Conditional Inference.- Basic Large Sample Theory.- Quadratic Mean Differentiable Families.- Large Sample Optimality.- Testing Goodness of Fit.- General Large Sample Methods.

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