Rating: 
Summary: Excellent textbook for introductory to modern statistics
Review: If you have basic training in calculus, you'll love this well written and easy-to-follow book. The book is almost good for self-study. It provides a good introduction to theoretical statistics with good examples.
Compare to many badly written mathematics books by famous mathematicians that gave me terrible experiences, I strongly recommend this book. As I was reading this book, I constantly recalled the hard time I experienced when I used Royden's "Real Analysis" or M. Artin's "Algebra". These two books are classical math textbooks that are appraised by many mathematicians. But according to my knowledge, many students extremely hate these two textbooks simply because these two books are hard to follow unless you read other textbooks. In my eyes, these "bad" textbooks are good only for those who have already mastered the contents (for exmaple, professors who have been teaching this subject for their entire lives). As for me, after I completely understood the topics, I found these two books are quite useful as reference books. But still I believe these two books are not good for entry-level students if they know 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 this the topics. Moreover, the content of this book is not simple, it contains almost all of modern 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, and 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 good educators, They know students, they know at what point students would encounter difficulty and at this point, the readers will find an appropriate example to help them out. After reading many bad mathematics textbooks, I believe that mathematician are trying to make our lives more miserable, and this is one of the reasons I lost my interests in mathematics, though I am always good at math. While reading "Statistical Inference", I fell in love with statistics, I'm convinced that statisticians are trying to make our lives better. As I was 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 in statistics. Compare to majority entry-level PhD students in statistics, I have much stronger back ground in mathematics (Lebesgue Measure, Integration and Differentiation), 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 absolutely 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 explanation, 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 inference since scientists in this particular field are trying to make our lives better and easier.
If you indeed want to learn statistics while having no strong specific back ground, I strongly recommend Casella Berger's "Statistical Inference"!
Rating:
Summary: Excellent textbook for introductory to modern statistics
Review: If you have basic trainings in calculus, you'll love this book. The book is almost good for self-study. It's very well written and easy to follow. This book provides a good introduction to theoretical statistics with good examples.
Compare to many badly written mathematics books by famous mathematicians that gave me terrible experiences, I strongly recommend this book. As I was reading this book, I constantly recalled the hard time I experienced when I used Royden's "Real Analysis" or M. Artin's "Algebra". These two books are classical math textbooks that are appraised by many mathematicians. But according to my knowledge, many students extremely hate these two textbooks simply because these two books are hard to follow unless you read other textbooks. In my eyes, these "bad" textbooks are good only for those who have already mastered the contents (for exmaple, professors who have taught this subject for their entire lives). As for me, after I completely understood the topics, I found these two books are quite useful as reference books. But still I believe these two books are not good for entry-level students if they know 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 this book. Moreover, the content of this book is not simple, it contains almost all of modern 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, and the students, without any working, are happy to wrongly believe that they know everything while they don't at all!). "Statistical Inference" is good only because it is carefully written. Casella-Berger are not only outstanding researchers, they are also good educators, They know students, they know at what point students would encounter difficulty and at this point, the readers will find an appropriate example to help them out. After reading many bad mathematics textbooks, I believe that mathematician are trying to make our lives more miserable, and this is one of the reasons I lost my interests in mathematics, though I am one of the best students according to many professors. After I finished the reading of Statistical Inference, I immediately fell in love with statistics, I believe statisticians are trying to make our lives better. While I was 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 in statistics. Compare to majority entry-level PhD students in statistics, I have much stronger back ground in mathematics (I mastered the subject of Lebesgue Measure, Integration and Differentiation), 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 topic, and this is absolutely 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 explanation, 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 inference since scientists in this particular field are trying to make our lives better. For those who indeed want to learn statistics and who have no strong specific back ground, I strongly recommend Casella Berger's "Statistical Inference"!
Rating:
Summary: very good, but it is difficult material for many people
Review: If you've read a lot of reviews pertaining to texts often used in difficult courses such as physics, grad level math & stats, etc, then you know that invariably a student victimizes a book during the semester. As a student and an instructor, I can safely say that the grade often correlates with the opinion of the book, usually the opinion of the book follows the student's perception of his or her grade in the class. This book is used for some difficult classes, but I can assure you, without being too specific, that it is well written. This material is difficult. I'm not a stats wizard, but I do like the subject area. I will say this: having been a graduate student at the University of Florida, any of the texts authored by the stats faculty there are excellent (ie: Agresti, Mendenhall, Khuri, Casella, and others). It is an outstanding faculty and I have had lectures from all of those professors. Reading their texts is just as clear as listening to them during office hours. My main point is that this is a very good text book. If you want it as a reference, get it. If you want it to supplement another text for your stats class, get it. Just make sure that you have a professor equally as knowledgeable about stats as the author, otherwise, it may be difficult to get help with difficult sections. I suspect the student that wrote from San Diego does not have an instructor that either follows this text closely, or adequately understands the material (I hope it is not the latter). Get this book and keep it around, you will reference before, during, and many years after your studies are through.
Rating:
Summary: Outstanding though challenging intro to math. stat.
Review: IMHO the best introduction to Probability Theory and Inferential Statistics. Because it doesn't say "Mathematical Statistics" in the title I ignored it for years and iterated between several other good texts. But Casella & Berger is more accurate, more up-to-date, and/or more fun to read. It strikes a better balance among topics and among schools of thought. It is furthermore exceptionally lucid and original, and very carefully edited. The organisation of the text is perfectly coherent, but this doesn't make it easy to skip difficult parts or concepts. The use of the book is also somewhat constrained by the author's effort at using nonstandard and challenging examples and problems (euphemistically called exercises). In practice I have to provide standard exercises to (econometrics) students as additional material. I am slightly uneasy with the unequal treatment of some items, many being emphasized as numbered propositions whereas others are just mentioned in the text. I similarly regret the cursory treatment of asymptotic distributions and asymptotic efficiency (for the purposes of econometrics). I do not like the exposition of Analysis Of Variance, but on the other hand I marvel at the stimulating treatment of linear regression in the last chapter. Quibbles apart, Casella & Berger is a demanding but most rewarding and stimulating introduction to (so-called) mathematical statistics, and in particular it is exceptionally dependable and witty. Beginning students may require some complementary material in the form of standard exercises and worked-out examples.
Rating:
Summary: Outstanding though challenging intro to math. stat.
Review: IMHO the best introduction to Probability Theory and Inferential Statistics. Because it doesn't say "Mathematical Statistics" in the title I ignored it for years and iterated between several other good texts. But Casella & Berger is more accurate, more up-to-date, and/or more fun to read. It strikes a better balance among topics and among schools of thought. It is furthermore exceptionally lucid and original, and very carefully edited. The organisation of the text is perfectly coherent, but this doesn't make it easy to skip difficult parts or concepts. The use of the book is also somewhat constrained by the author's effort at using nonstandard and challenging examples and problems (euphemistically called exercises). In practice I have to provide standard exercises to (econometrics) students as additional material. I am slightly uneasy with the unequal treatment of some items, many being emphasized as numbered propositions whereas others are just mentioned in the text. I similarly regret the cursory treatment of asymptotic distributions and asymptotic efficiency (for the purposes of econometrics). I do not like the exposition of Analysis Of Variance, but on the other hand I marvel at the stimulating treatment of linear regression in the last chapter. Quibbles apart, Casella & Berger is a demanding but most rewarding and stimulating introduction to (so-called) mathematical statistics, and in particular it is exceptionally dependable and witty. Beginning students may require some complementary material in the form of standard exercises and worked-out examples.
Rating: 
Summary: As a student...I hate this book
Review: In fact, my class is almost having a revolt over this text. We are in a first year graduate course for "Advanced Statistical Methods". The class universally sees this book as useless. It may as well be written in Swedish. No examples are given.
A good theoretical book should still present the
reader with real life applications of the theories
presented. This book does not. The explanations assume the reader knows too much.
Students can't even test their own knowledge by practicing problems, since no answers are given
in the back of the book. May be a better text for a PhD level course, but
as an MS student, I (and my entire class) would
not recommend using this book for your class.
Rating:
Summary: A good book with a few weak points..
Review: Like many statisticans, I used this book in my Grad program. Needless to say, I've read the book from cover to cover many, many times. As theory goes, I think this book is excellent. However, I believe the major weakness of this books lies in it's examples and problem sets. I believe that (even for advanced texts) the problem sets should have a difficulty gradient to them (starts out with easier problems and ends with the real brain twisting tough problems), and this books does seem to do that to a degree, but it does not do it very well. In addition to this, there are many problem sets in the book where it is very easy to get lost in the math and completely miss the important statistical point/lesson that should be illustrated. Many of the most difficult problems of the book have very little to do with statistics and more to do with mathematics. The authors also have the annoying habit of refering to the results of previous problems/excercises. Therefore, in order to do some exercises/examples, you must go back and work one or two of the exercises from one of the previous chapters. The book would have been a lot more helpful if the author would provide the solutions for exercises that he intends to build upon.
Rating: 
Summary: A Good Book
Review: Overall, this book is a solid introduction to mathematical statistics. Exposition is clear and it fully motivates all concepts. I really only have one complaint: this book omits a few topics. A relatively minor example is the absence of the cumulant generating function. More disturbingly is that it does not have a full discussion of the multi-normal distribution (possibly to avoid some non-trivial linear algebra?). However, being that the book is otherwise quite complete and these topics can be found elsewhere these are rather annoyances than fatal flaws.
Rating: 
Summary: Good book... for a PhD student
Review: This book, is not for newcomers to statistics. It often assumes a well versed backround in probability and statistics (and I suppose a decent background in calculus). There is often little explanation for examples and theorems. Often the exercises and examples require the solution to previous exercises. This is extremely frustrating and often discouraging. Many times I would find myself being refered to an example in a previous or future chapter whose solution refers to a problem that needs to worked out. I have an undergraduate degree in math and am by no means a genius, but compared to many well written math texts, the clarity and conciseness leaves much to be desired. The exercises do not go from easier to more difficult but from difficult to difficult. Often the concept that is trying to be illustrated could be done in a much simpler problem.
That all said, the book is fairly well written given the aforementioned weaknesses. My professors seem to like it as do a couple of PhD students.
Rating:
Summary: good textbook for intro grad
Review: This is a good textbook designed for introductory stat or econ phd classes. This book assume a decent knowledge in calculus which ought to be expected by those taking the course. Advanced calculus, eg real analysis, is not really required. Some questions are somewhat long and tedious, but the difficulty ought to be expected.
|
