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Theory of Point Estimation (Springer Texts in Statistics)
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Binding: Hardcover
Dewey Decimal Number: 519.544
EAN: 9780387985022
Edition: 2nd ed. 1998. Corr. 4th printing
ISBN: 0387985026
Label: Springer
Manufacturer: Springer
Number Of Items: 1
Number Of Pages: 589
Publication Date: September 09, 2003
Publisher: Springer
Studio: Springer
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Editorial Review:
This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated. An entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation. Each chapter concludes with a Notes section which contains suggestions for further study. The book is a companion volume to the second edition of Lehmann's "Testing Statistical Hypotheses". E.L. Lehmann is Professor 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. George Casella is the Liberty Hyde Bailey Professor of Biological Statistics in The College of Agriculture and Life Sciences at Cornell University. Casella has served as associate editor of The American Statistician, Statistical Science and JASA. He is currently the Theory and Methods Editor of JASA. Casella has authored two other textbooks (Statistical Inference, 1990, with Roger Berger and Variance Components, 1992, with Shayle A. Searle and Charles McCulloch). He is a fellow of the IMS and ASA, and an elected fellow of the ISI. Also available: E.L. Lehmann, Testing Statistical Hypotheses Second Edition, Springer-Verlag New York, Inc., ISBN 0-387-949194.
Customer Reviews
Average Rating: 
Rating:  - highly theoretical text
Erich Lehmann wrote this classic text on point estimation as the companion to his earlier masterpiece on hypothesis testing. The book lived up to the standard set by the other text. This edition, now published by Springer-Verlag was revised with the help of George Casella who is also an excellent writer. Another great reference book that statisticians should have on their bookshelf.
Rating:  - Very thorough book, but contains many errors
I have been using this book for my graduate studies for several years. It is an advanced textbook on point estimation and an important bridge to the vast literature on the subject. In my experience, it is the single best book on the insides of point estimation. It is an invaluable reference book; I would be unable to do my research without it.
That said, it has two major problems:
1) It has many, many errors. The errata range from incorrect cross-referencing (very strange, ... Read More
Rating:  - Sloppy, unclear, misleading: unfit for use as a textbook
I originally gave this book a much more generous review, but after having worked with it extensively, I have revised my review. This high-level text is aimed towards people in Ph.D. programs and theoretical researchers in statistics and related areas. The second edition of this book seems hastily thrown together, especially as one gets into the later chapters. It is difficult to use and may be misleading to many students. Its only justifiable use, in my opinion, is as a survey of the literature ... Read More
Rating: - Excellent book
I am a bit surprised by the rating of this book. First of all, I will like to say, this book is intended for doctoral study or someone who are intersted in the theory of statistics. If you are looking for something basic to implement your projects, this is not the book for you.
The proofs in the books may be "sketchy", however, as I've mentioned earlier, for those who are taking class, should have taken some of the basic theories course. And, one thing I will like to mention, measure ... Read More
Rating:  - Good book
Good book, but a little abstract for me. It requires some knowledge of measure theory. Some definitions and theories are stated in a more "mathematical" language. "Statistical Inference" by George Casella would be a good reference for it.
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