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Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics) Hardcover – March 25, 1993

ISBN-13: 978-0387960982 ISBN-10: 0387960988 Edition: 2nd

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Product Details

  • Series: Springer Series in Statistics
  • Hardcover: 618 pages
  • Publisher: Springer; 2nd edition (March 25, 1993)
  • Language: English
  • ISBN-10: 0387960988
  • ISBN-13: 978-0387960982
  • Product Dimensions: 1.3 x 6.1 x 9.2 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #794,940 in Books (See Top 100 in Books)

Editorial Reviews

Review

"The outstanding strengths of the book are its topic coverage, references, exposition, examples and problem sets... This book is an excellent addition to any mathematical statistician's library."
(Bulletin of the Am. Mathematical Soc.)

About the Author

James O. Berger teaches at the Institute of Statistics and Decision Sciences, Duke University.

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Customer Reviews

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This book is theoretical but it has enough examples to follow along.
Steve Chung
If you are going to buy any one advanced, theoretical book on statistics, this would be the one to buy.
Alexander C. Zorach
It is a classical book written by an excellent mathematician, not a worker of statistics!
supercutepig

Most Helpful Customer Reviews

42 of 42 people found the following review helpful By Alexander C. Zorach on November 10, 2007
Format: Hardcover Verified Purchase
This book (which is becoming somewhat of a classic) is simply outstanding. The author's philosophical and mathematical reasoning is impeccable. This book is very deep and will not just teach you theory and techniques but will teach you how to think and how to approach and formulate problems, as well as how to interpret the results obtained by various methods and use them in a practical setting. This book is very practically oriented. The best thing about this book, however, is that it is so clearly written. Berger is an outstanding writer with the ability to communicate difficult concepts without watering them down.

This book covers decision theory and Bayesian statistics in much depth. While it is a high-level text oriented towards researchers and people with strong backgrounds, it is clear enough that someone learning this material for the first time would have little trouble with it. It provides ample review and clear exposition of key mathematical and statistical concepts such as sufficiency, convexity. Its exposition of invariance (with respect to groups of transformations) is both the clearest and most rigorous I have found in any statistics text. In my opinion, there are no weak or unclear sections in the book, and the difficulty level does not rise disproportionately in later chapters the way it does in many books on similar subjects.

This book is rich with examples, and the examples are mostly of a practical nature, in contrast to the "toy" mathematical examples that dominate many books written at this advanced level. The exercises are diverse and extensive, and have a good gradient of difficult level for building both technical skill and depth of understanding. The exercises are more carefully worded and constructed than is typical for books at this level.
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41 of 43 people found the following review helpful By supercutepig on August 30, 2005
Format: Hardcover
[1] It is a classical book written by an excellent mathematician, not a worker of statistics!

[2] Its mathematics is precise and fascinating.

[3] The philosophy of Bayesian statistics is well discussed.

[4] It's worthy to read it many times.

[5] At the time of its publication, the revolutionary computational statistics was still in gestation. So, it is unfair to criticize its lack of numerical simulation, etc. As a comlement, some pragmatistic books are recommended, such as J. Liu's book on MCMC methods, Tanner's Tools for Statistical Inference, etc.
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31 of 33 people found the following review helpful By Michael R. Chernick on February 22, 2008
Format: Hardcover
Jim Berger is well known for his work in decision theory, Bayesian methods amd his texts on these subjects. This one is certainly one of the best to cover decision theory and give a rigorous treatment to Bayesian methods, particularly in the context of decision theory.
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24 of 27 people found the following review helpful By Gongfu Zhou on June 14, 2001
Format: Hardcover
The professor used this book in the math stat course I took 2 years ago. I did not like the book at first, it looks too long to be covered. but it turned to be very easy to follow (You still need to think, but the author, being a hero in this field, made a very clear presentation to the audience). As long as you invest some time and brain, you can get a lot from this book. The problems are very good and instructional too.
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26 of 30 people found the following review helpful By Steve Chung on December 21, 2004
Format: Hardcover
This book is awesome. This book is theoretical but it has enough examples to follow along. The author's presentation is clear in every step. The problems in the book are challenging but do-able. Don't need another reference book.
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