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Statistical Inference 2nd Edition
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Top Customer Reviews
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.Read more ›
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.
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.
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.Read more ›
Most Recent Customer Reviews
I would purchase this book for chapters 1 through 7, but I felt like exercises start to go down hill starting at chapter 8 and beyond. Read morePublished 4 months ago by Cogsbox6.28
Obviously this book is great, but I consider it more as a reference rather than as an instructional text. Read morePublished 6 months ago by D Marx
I finally got to reading this book, and I would say I was familiar with everything in this book except the sufficiency section. Read morePublished 6 months ago by Yeng C.
For what it is its ok. I am not a fan of this book, really hard to read and poorly layed out. If it wasnt required for my course I would have given it 0 stars. Read morePublished 9 months ago by Patrick
Despite the boring title, this is actually an excellent primer on how Big Data works. Which is of interest to more than just statisticians. Read morePublished 12 months ago by Ditch
I used it in my summer math camp for economics students. The book is comprehensive. If you have a good command of calculus sequences (integration by parts, for example) and basic... Read morePublished 13 months ago by Alvin
The content and presentation of this book is well laid out and complete. However, there are massive inconsistencies in the actual form of different distribution functions used... Read morePublished 15 months ago by Charlie