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Statistical Inference, by George Casella, Roger L. Berger
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This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
- Sales Rank: #98805 in Books
- Brand: Brand: Cengage Learning
- Published on: 2001-06-18
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.20" w x 6.50" l, 2.20 pounds
- Binding: Hardcover
- 660 pages
- Used Book in Good Condition
Review
"Statistical Inference is a delightfully modern text on statistical theory and deserves serious consideration from every teacher of a graduate- or advanced undergraduate-level first course in statistical theory. . . Chapters 1-5 provide plenty of interesting examples illustrating either the basic concepts of probability or the basic techniques of finding distribution. . . The book has unique features [throughout Chapters 6-12] for example, I have never seen in any comparable text such extensive discussion of ancillary statistics [Ch. 6], including Basu's theorem, dealing with the independence of complete sufficient statistics and ancillary statistics. Basu's theorem is such a useful tool that it should be available to every graduate student of statistics. . . The derivation of the analysis of variance (ANOVA)F test in Chapter 11 via the union-intersection principle is very nice. . . Chapter 12 contains, in addition to the standard regression model, errors-in-variables models. This topic will be of considerable importance in the years ahead, and the authors should be thanked for giving the reader an introduction to it. . . Another nice feature is the Miscellanea Section at the end of nearly every chapter. This gives the serious student an opportunity to go beyond the basic material of the text and look at some of the more advanced work on the topics, thereby developing a much better feel for the subject."
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Overall, this book is a solid introduction to mathematical statistics. Exposition is clear and it fully motivates all concepts. I really only have one complaint: this book omits a few topics. A relatively minor example is the absence of the cumulant generating function. More disturbingly is that it does not have a full discussion of the multi-normal distribution (possibly to avoid some non-trivial linear algebra?). However, being that the book is otherwise quite complete and these topics can be found elsewhere these are rather annoyances than fatal flaws.
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