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| Statistical Inference | 
| Authors: George Casella, Roger L. Berger
Publisher: Duxbury Press
Category: Book
List Price: $176.95
Buy New: $74.92
You Save: $102.03 (58%)
New (30) from $74.92
Avg. Customer Rating:  33 reviews
Sales Rank: 9628
Media: Hardcover
Edition: 2
Number Of Items: 1
Pages: 688
Shipping Weight (lbs): 2.3
Dimensions (in): 9.2 x 6.5 x 1.1
ISBN: 0534243126
Dewey Decimal Number: 519.5
EAN: 9780534243128
ASIN: 0534243126
Publication Date: June 18, 2001
Availability: Usually ships in 1-2 business days
Condition: New Book, Hardcover. Same Edition As Amazon's Description! Never Been Read! Buy Now!
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Product Description
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. This book can be used for readers 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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| Customer Reviews: Read 28 more reviews...
 GOOOOOOOD September 20, 2008
the book was delivered in a few days and the condition of the book was good.
 Good introduction, many errors June 8, 2008
2 out of 2 found this review helpful
This text is quite good, with numerous examples, but beware of the many errors or cases of sloppy reasoning. A sampler:
p. 319. The maximum likelihood estimator for the binomial distribution, unknown number of trials, is unique. Not true: n=2, p = .4, sample = (1,6) is a counterexample.
p. 265. If S is the sum of k idd uniform (0,1) random variables, then Prob(S <= t) is t^k over k!. Not true: this would give prob(S <=k) > 1.
p. 62, 82, 84: Moments are unique (or non-unique). Nonsense, it is the pdf's that are unique or non-unique.
p. 444. Method to find a shortest pivotal interval. This is a non-proof. Apparently the authors haven't heard of Lagrange multipliers.
Note also that apparently there's no source for problem answers. This may or may not be considered a drawback.
Great textbook. December 4, 2007
8 out of 8 found this review helpful
This is a fantastic book. It is very well written and is a pleasure to read. The problems at the end of each chapter are extensive and help get a very good understanding of the material. This was the required text for a quarter based graduate level course on Statistical Inference. We had an excellent teacher who picked problems very well and that perhaps kept us from getting bogged down. Many of the problems are by no means trivial and require time to solve, which is where a great instructor helps. If you are planning to use this book for self-study, then I would recommend perusing the problem sets from classes, based on this book, that are being offered at some institutions, in order to whittle down the problems to an illustrative subset, before delving into others. Hope this helps.
 Don't believe it! October 20, 2007
9 out of 17 found this review helpful
This book is absolute misery! I would like to echo another review that basically stated if you have to take a class with this book, just drop it now and save yourself the grief. Truer words were never spoken! The Preface states that the prerequisite is 1 year of calculus. That is an outrageous lie! Maybe if you took calculus at Princeton or MIT, you will have a fighting chance. Otherwise you better have the sophistication of writing and understanding proofs that are on par with a real analysis background, and you will definitely need a firm grasp of all the major combinatorial identities and proof techniques before you even attempt to read it, let alone destroy your GPA with it! There is a solution manual floating around the internet, and that too is worthless. Most of the proof techniques used in that rotten book end up as handwaving, and if you have a well trained professor, you will get crushed trying to use some of those techniques. Many of the answers in the solutions manual are just wrong as my professor has PROVEN to us on a number of occasions. The bottom line is dont believe anyone who tells you that 1 year of calculus is enough to read and understand this book. It simply does not apply to most of us, and Casella and Berger should be ashamed of themselves for trying to pass this off as a first year graduate textbook for anyone other than a pure mathematician.
To further highlight the absurdity of this book, here is a quote from p 237: "Furthermore, with the current availability of cheap, plentiful computing power, the importance of approximations like the Central Limit Theorem is somewhat lessened." Que idiotas!!!!!
good text for first graduate course in statistics October 15, 2007
22 out of 22 found this review helpful
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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