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.

CUSTOMER REVIEWS (Average Customer Rating: 4.0 based on 34 reviews)

Good Introduction to Probability Theory, Mathematical Statistics and Estimation

I'm using this textbook for a first year PhD Introduction to Econometrics class and I'm enjoying the clear presentation, rigorous treatment and elegant typesetting. (The text includes Mathematica code on which I can't comment at this time, but the inclusion of code in such a text is encouraging in itself.)
October 25, 2008

GOOOOOOOD

the book was delivered in a few days and the condition of the book was good.
September 20, 2008

Good introduction, many errors

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.

June 08, 2008

Great textbook.

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.
December 04, 2007

Don't believe it!

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!!!!!
October 20, 2007

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