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Decision Theory: Principles and Approaches 1st Edition

by Giovanni Parmigiani (Author), Lurdes Inoue (Author)
5.0 out of 5 stars 2 customer reviews
ISBN-13: 978-0471496571
ISBN-10: 047149657X
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Product Details

  • Hardcover: 404 pages
  • Publisher: Wiley; 1 edition (May 26, 2009)
  • Language: English
  • ISBN-10: 047149657X
  • ISBN-13: 978-0471496571
  • Product Dimensions: 6.4 x 1.1 x 9.2 inches
  • Shipping Weight: 2.1 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #1,597,128 in Books (See Top 100 in Books)

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5.0 out of 5 starsGreat decision theory textbook
By Richard Hahn on September 20, 2010
This textbook covers all of the basics in decision theory. It is clear and thorough. One especially nice thing about this book is that it collects several results in one place that aren't covered in much detail in many statistics texts: the Stein phenomenon, complete class theorems, and state-dependent utilities, to name a few. I'd pair this with Schervish's Theory of Statistics and Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models for a three-pack covering the core of Bayesian statistics -- decision theory, statistical theory and applied regression modeling.
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5.0 out of 5 starsDecision Theory with a Bayesian orientation
By Michael R. Chernick on February 25, 2010
There have been many books on decision theory going all the way back to Wald in the 1940s. We have the texts by Blackwell and Girshick, Luce and Raiffa, Raiffa and Schlaifer, DeGroot, Berger, Wald, Ferguson, and Chernoff and Moses to name some of the classics. Parmigiani and Inoue provide an up-to-date acount on the subject. New ultiity fuinctions such as QALY (quality of life years) are introduced and the chapters 2-6 provide the foundations of the Bayesian approach to decision theory. Then chapters 7-11 provide the theory from the Bayesian viewpoint. They include the concept of admissibility and Stein's famous shrinkage estimators that show the maximum likelihood estimator for a multivariate mean vector is not admissible (3 or more dimensions).

The third section titled Optimal Design deals with multistage decision problems and introduces dynamic programming as a technique to solve some of these problems.
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