Statistical Inference

Great textbook. It covers the most important topics gently building oneach other. The pace of the book is excellent and the writing style is easy to follow - almost addictingly so. It also covers the subtle points that are often left out in normal courses.

This is a good book for teaching and reference. It is not perfect, but it is very helpful.

No major issues, its obvious that the seller tried to hide the fact that the book should only be sold in india/pakistan/etc... but this is not the buyer's risk. Im overlooking this. The binding is fine, the pages themselves are a bit thin, so use a pastel highlighter instead of a standard one to make notes. Some pages are printed at a slight angle(the text is not perfectaly straight with the page) but its not problematic. In some parts, the printed ink is a bit diffuse, but still fully legible. My only caveat is that the text seems to get too close to the binding, so you have to stretch the book a bit to see it better. Either i got lucky with my printed copy, because the other reviews seem to be blowing some of the issues of out of proportion. If you need the book for studying, it'll serve its purpose just fine. If you really insist on better paper quality and perfect allignment/ink print, then buy the more expensive versions.

Okay

The price is really competitive and the quliaty of the book is really high. The package is great and no impairment occurs

A thorough description of statistical theory from the fundamental premices to the major results. Progress throughout the book is carefully structured, with thought even given to the numbering of theorems and examples so that the reader feels comfortable and progress is easy to follow. There is little digression into other areas of maths, with a little vector notation being as hard as it gets. As a result, the prerequisites are few and the flow is uninterrupted. Since Amazon don't, at time of writing, have a table of contents for this book, I'll give a brief run down: Simple probability ideas - intersection/union of events, conditional probability and Bayes probability; random variables - general relationships, functions, etc.; families of distributions - normal, gamma, exponential, etc.; random sampling - theorems, implications, expectation/variance, etc.; normal random variables - implications of the particularisation of previous theories; point estimation theory - how to generate an estimator and how to judge performance; hypothesis testing - formulating and evaluating tests; interval estimation - generating confidence intervals for point estimates, relationship to hypothesis testing. There are one or two other chapters, but these are the only ones I've read thoroughly. There's no reason to believe the others aren't as impressive as these, though.

This book provides an excellent introduction to statistical inference and takes a fairly rigorous approach beginning with set theory and introducing the basic axioms of probability theory before introducing distributions. Despite being aimed at graduate students the prerequisites are fairly modest - the authors suggest a year of calculus plus perhaps some exposure to matrices. The written style is engaging and the authors take the trouble to lead the reader through the development of the material. Again, a pleasant surprise for a book aimed at this audience is that it does not avoid explaining straightforward algebraic manipulations or calculus rules used in the derivations presented - these can often trip up any mortal reader and it is refreshing to have these additional notes so that one is not distracted from the important mathematical point by failing to follow the thread of the argument. There are also copious examples and a huge quantity of exercises (presented without answers). Unfortunately this book is let down by poor printed presentation: my copy features pages with text that is in some places faint and occasional not printed at all; printing is often not aligned with the page with lines running at an angle of several degrees to the page edge, and some pages are not cut from their neighbours. This is a pity as the written content is thoughtfully presented and is otherwise a valuable addition to the bookshelf of many mathematicians, perhaps particularly those with a solid grasp of mathematics but less so a background in statistics.

Pages very thin and print faint in places. Good information and useful for academic purposes or self interest

The book arrived well packaged and in good condition. The text has so far all been legible. There are some very minor dried blotches of ink on some pages, but they are very small and in no way obstruct the text. Saw that others have had printing issues with their copies, but mine is well made. Would recommend as a cheaper alternative.

One of the most foundational books for statistics. A must have for any statistician.

勿論まだ読了前。

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. The students, without any working, are happy to wrongly believe that they know everything while they don't). "Statistical Inference" is good only because it is carefully written. Casella-Berger are not only outstanding researchers, they are also excellent educators. They know students, they know at what point students would encounter what difficulty and at this point, you definitely will find an appropriate example to help you out. The sharp contrasts between "Statistical Inference" and many "bad" textbooks in mathematics convince me that mathematicians are trying to make our lives more miserable (and this is one of the reasons I lost my interests in mathematics, though I have been good at math) while statisticians are trying to make our lives easier. At the same time of going through "Statistical Inference", I was also reading Richard Durrett's "Probability: theory and examples", a widely used typical textbook in probability for first year PhD student. Compared with majority entry-level PhDs in statistics, my background in mathematics (Lebesgue Measure, Integration and Differentiation) is no weaker, yet I experienced the same hard time as I did in some other math classes. My blame can only go to the bad written textbook, I have to read other textbook to understand the topics, and this is not good for a not-stupid and hard working student. I am always curious that among all the textbooks available, why mathematicians prefer the textbooks that will give students more hard time. For the same topic, using different approaches, students will have different feelings, why can't the professor pick up the more friendly written books for the sake of student's easy understanding and their continuing interests in the area? My belief was strengthened after completing the reading of Casella-Berger's "Statistical Inference" and R. Durrett's "Probability", that one must keep away from mathematicians as far as possible since your life will be tough if you are close to them. And as for myself, I won't do research in probability since the book "Probability" gave me the impression that more mathematicians are involved in the area of probability theory. I'll go with Casella & Berger, concentrate on the filed of statistical inferences since scientists in this particular field are trying to make our lives better and easier. In conclusion, if you need to learn statistics while having no specific back ground, I strongly recommend Casella Berger's "Statistical Inference"..

Un libro útil y referencia obligada.