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Statistical Inference [Hardcover]

George Casella , Roger L. Berger
3.6 out of 5 stars  See all reviews (47 customer reviews)

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Book Description

June 18, 2001 0534243126 978-0534243128 2nd
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.

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Editorial Reviews


"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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Product Details

  • Hardcover: 660 pages
  • Publisher: Cengage Learning; 2nd edition (June 18, 2001)
  • Language: English
  • ISBN-10: 0534243126
  • ISBN-13: 978-0534243128
  • Product Dimensions: 9.3 x 6.5 x 1.2 inches
  • Shipping Weight: 2.1 pounds (View shipping rates and policies)
  • Average Customer Review: 3.6 out of 5 stars  See all reviews (47 customer reviews)
  • Amazon Best Sellers Rank: #32,611 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
105 of 112 people found the following review helpful
Although not particularly advanced, this book is quite specialized and in my opinion, too narrowly focused for a book at this level. It is not a comprehensive introduction to statistical inference. Also, it often focuses on the "what" and the "how" while ignoring the "why".

The book's strengths are self-evident. The exposition of probability theory is excellent, and presented with an eye towards its use in statistics. The mathematical aspects of this book are clean and thorough, and the omissions of certain difficult proofs enhance rather than detract from the book's quality. But as one progresses further in this text, there are many shortcomings. The order in which topics are presented doesn't always seem natural to me.

My main criticism of this book is that it presents a narrow view of what statistics is, and as such I think it is misnamed; "Statistical Inference" encompasses much more than what this book covers. This book is really about "classical" statistics and it does not acknowledge or integrate more modern ways of looking at things, even when they could be presented at an elementary level. The Bayesian paradigm is hardly mentioned, non-parametric approaches are hardly mentioned, and decision theory is ignored. As such, I don't see how it offers any improvement over older texts, such as Hogg and Craig.

My second criticism of this book is that it is divorced from applications; there is almost no data presented in the text or problems. Discussion of modeling is almost completely absent, and the material on distributions in chapter 3 doesn't probe very far into the particular reasons why certain distributions arise in certain situations.
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51 of 56 people found the following review helpful
5.0 out of 5 stars good text for first graduate course in statistics October 15, 2007
This is the second edition of an excellent book. Casella and Berger put together a text that many faculty began choosing for the first graduate course in mathematical statistics. This second edition is improved over the first and puts more emphasis on the algorithms than the asymptotics. It covers modern topics like resampling and is verywell presented.

When I was a graduate student we used Ferguson and Cox and Hinkley and we also used Lehmann's book for hypothesis testing. This book starts with basic probability and goes on to cover all the bases. It has everything one needs in a modern text on mathematical statistics. I have seen it referenced very often in statistics articles and I decided that I had to get a copy for myself in spite of the high price. i think this should be one of the preferred texts for the first year PhD course in mathematical statistics. It certainly requires a full year of calculus as would any good math stat book but the level is even higher than that and that also should be expected by the students.

Typically first year PhD students in statistics would take this course concurrently with a course in advanced probability that includes measure theory. So the measure theory knowledge gained by the student in the probability course will and should be needed for the latter chapters of this math stat course.
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72 of 87 people found the following review helpful
5.0 out of 5 stars Excellent on introduction to univariate statistics June 16, 2003
By J. Wang
Format:Hardcover|Verified Purchase
If you have basic training in calculus, you'll love this well written, easy-to-follow book. It provides a complete list of theories along with rigorous proofs and comprehensive examples, by which it is almost good for self-study.

Comparing with many badly written mathematical books by famous names that gave me terrible experiences, I strongly recommend this book. As I was enjoying reading of this book, my memory constantly went back to the difficult time I had experienced when I tried so hard on Royden's "Real Analysis" or M. Artin's "Algebra". These two are classical math textbooks that are appraised by the majority of mathematicians. But from my observation, quite a few students hate these two books to some extreme, because they are so hard to follow unless you read other textbooks. In my opinion, these "bad" textbooks are good only for those who have already mastered the contents (for example, professors who have been teaching this subject for their entire lives). After completely understood the topics, I found these two books are quite useful as reference books. But still I do not think these two books are good to begin with if the reader knows little about the subjects in the books. As contrary, Casella-Berger's book is very good for entry-level students. Good knowledge in calculus is sufficient for you to easily follow the topics. Moreover, the content of this book is not simple; it contains almost all aspects of univariate statistics. (many poor calculus books are written in such a way that in order to please the students, the author intentionally omitted some important subjects and/or reduced the level of the contents. By doing so, the author became famous and the book went to best-selling.
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22 of 25 people found the following review helpful
3.0 out of 5 stars Review by a Grad Student March 27, 2010
By E. Ward
I used this book as a first year graduate student in statistics. My undergrad degree was not in statistics, I think those that had an undergraduate degree in statistics enjoyed this book more. My main problem with it was that after this book I knew HOW to do a problem, or WHAT a theorem was, I could PROVE and DERIVE things, but I was really lacking in UNDERSTANDING. I had to spend many hours asking questions of my professors about what all the things I was doing actually meant, or why I was doing them. As I learn more about statistics, this book becomes better because I can put it all in context. Just last semester, I probably would have said I didn't like this book at all, but now I'd say it's okay. Just note it's shortcomings.

Also, I have the paperback "international" version. The international version has VERY cheap paper and a lame binding. If you are going to spend as many hours as I did with this book, you should just suck it up and buy the expensive hardcover version. The paper for the paperback is practically see-through and makes it tougher to read.
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Most Recent Customer Reviews
5.0 out of 5 stars Five Stars
Published 10 days ago by Sandra Adams
3.0 out of 5 stars Pricy
The book is very qualified, good explanations and everything, but it is incredibly expensive when you compare the price and your expectations...
Published 4 months ago by Berna Yazici
1.0 out of 5 stars Current student/user of this text...unfortunately
I must disagree with the esteemed formal review of this text. It is decidedly the clear opposite of delightful. Read more
Published 4 months ago by Soula
2.0 out of 5 stars Heavy on the Calcs
This book has some serious calculations in it. Overall, I think you should have a heavy mathematics background if you plan to use this book. Read more
Published 4 months ago by Econamust
5.0 out of 5 stars Readable, interesting book
This is the second statistics book I have used in a course (previous was wackerly). I found this book to be very readable. Read more
Published 7 months ago by Shawn B
1.0 out of 5 stars Bad
I am currently using this text for a first year stats course in a economics phd program and I am not pleased. While reading theorem, proofs, exercises, examples, etc. Read more
Published 9 months ago by Stephen
5.0 out of 5 stars Excellent book
I started learning Probability theory from Mood, Graybill and Boes "Introduction to Statistical Inference", 1974, almost one year ago. Read more
Published 9 months ago by Christian Ponce
3.0 out of 5 stars Decent Textbook for graduate studies - poor problem sets
Coming into graduate school with little theoretical background in statistics(I was a science undergraduate major who did applied statistics) I did not enjoy Casella and Berger. Read more
Published 13 months ago by Olorin
2.0 out of 5 stars It could be worse
Well, it's better than Bain & Engelhardt. Packed with definitions and theorems, but little direction on how to solve problems. Read more
Published 13 months ago by Blackeneth
4.0 out of 5 stars Graduate Level Text
This book is a graduate level textbook for Mathematical Statistics. I wouldn't suggest it as a first introduction to the material, but for those with some statistical background,... Read more
Published 18 months ago by hotdogs
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difference between 1st ed and 2nd ed
do you mean in general or do you have a specific text in mind?
Jun 30, 2008 by J. Duggins |  See all 3 posts
7000 pages, is it? Be the first to reply
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