Probability and Statistics (Pie): Amazon.co.uk: Morris H. DeGroot, Mark J. Schervish: Books

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Probability & Statistics was written for a one or two semester probability and statistics course offered primarily at four-year institutions and taken mostly by sophomore and junior level students, majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus. The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a new chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), expanded coverage of residual analysis in linear models, and more examples using real data.

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5.0 out of 5 stars (2 customer reviews)

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8 of 9 people found the following review helpful:

5.0 out of 5 stars Excellent - example laden and full of hands-on assignments, 19 Feb 2002

By

P. A. Murphy (London, U.K.) - See all my reviews
(REAL NAME)

This review is from: Probability and Statistics (Hardcover)

If, like me, you like to learn by doing, then this is the book for you. I bought this book when studying for a Computational Finance masters, and I'm now revising the subject matter by doing the questions and revising the examples. It beats having to re-read the whole thing.
If any book is worth �100, this one is.

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2 of 2 people found the following review helpful:

5.0 out of 5 stars Still one of the best statistics references around, 17 Jun 2009

By

C. Cafiero (Berkeley, CA. and Naples, Italy) - See all my reviews
(REAL NAME)

This review is from: Probability and Statistics (Pie) (Paperback)

Including a lucid presentation and comparison of classical and bayesian statistical theory and methods, this book is still one of the best teaching references available.
If you are looking for a single textbook in statistics for advanced undergraduate and introductory graduate courses, this is it.

Carlo Cafiero
University of Naples Federico II, Italy

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Amazon.com: 3.8 out of 5 stars (29 customer reviews)

82 of 85 people found the following review helpful:

5.0 out of 5 stars Good intro for self-study, 25 April 2003

By "self-study" - Published on Amazon.com

This review is from: Probability and Statistics (Paperback)

This is an introductory book. It also fits in introductory level of Mathematical Statistics. The prerequisites are introductory calculus and linear algebra. Most theorems are proved in calculus style but there are some �gIt can be showns�h that are not proved. So some readers may not be satisfied with the book, especially Math majors.

Logical steps are shown in detail; else logical gaps are contained within a level such that a first time reader can fill in the gap with a pencil and paper. Occasional mix with Bayesian perspective is also a feature. Answers to odd-numbered exercises are provided except ones that ask derivations and proofs. Exercises that require some tricks are provided with hints. In these respects, this textbook is suitable for self-study.

Upon completion of the entire material, I feel concepts are developed well up to Hypothesis testing Chapter 8 where the presentation of material reaches climax and its level of exposition is somewhat higher than other chapters. Thereafter, simple linear regression is treated in detail, but coverage and detail of materials seem to deteriorate from the following general regression section, nonparametrics and thereafter. Kolmogorov-Smirnov Tests section is treated nicely though. Anova section lacks in coverage. The new simulation chapter is presented more like a demonstration rather than an introduction.

I have never seen the previous 2nd edition (unfortunately Dr. Degroot is no longer with us), but according to the preface of this 3rd edition, Dr. Schervish describes 8 major changes from the previous edition. Notable are some material removed from the previous (likelihood principle, Gauss-Markov theorem, and stepwise regression), some added (lognormal distribution, quantiles, prediction and prediction intervals, improper priors, Bayes test, power functions, M-estimators, residual plots in linear models and Bayesian analysis of simple linear regression), more exercises and examples, special notes, introduction and summary to each section, and so on. I find the last in the list is somewhat disturbing, especially introduction parts that are often redundant with the very next paragraph. On the other hand, I find that special notes provide good insights.

I wish they included introduction to Statistical Decision theory, full coverage of regression analysis to be usable such as diagnosis, transformation and variable selection, coverage of Multivariate Normal distribution, more coverage and depth in nonparametrics and simulation, and lists of recommended readings for further study at the end of each section with comments.

There are a noticeable number of typos as of this first printing I have. I sent suggestions for typos and was impressed that Dr. Schervish updated errata list within a few days at his homepage. I wish all authors were like him being responsible.

47 of 51 people found the following review helpful:

3.0 out of 5 stars P(Grade > B) = Small Without A Curve, 9 Dec 2005

By Deep Roy - Published on Amazon.com

This review is from: Probability and Statistics (Paperback)

After having used this book in a graduate level probability/statistics course and having the opportunity to poll students who took that class over the past 3 years, I found out that the probability of getting a good grade, and achieving understanding with DeGroot, was small.

To my joy, the university now uses Fredrick Solomon's book entitled "Probability and Stochastic Processes" for their 4000 level course.

After reading Solomon's book, I found myself getting unconfused and after having studied Jim Pitman's Probability book and Freedman's Statistic's book, I can now get into DeGroot's book. I am also going to get Feller's book, volume 1. What I needed, and DeGroot didn't offer, was a better feeling of "number sense" or what I think of as the "physics of numbers." I also wanted to know about the connections between things (concept maps) and DeGroot didn't do this, initially, for me.

I agree with the other reviewers that DeGroot's book is interesting but I don't believe that DeGroot sequenced the information well or had the desire to bring out a lot of the hidden details. Of course, after I read the other books I mentioned, I am beginning to see how wonderful DeGroot is for the advanced learner because he puts things together in interesting ways. However, to get to that level of appreciation, and see the "deeper connections," I really needed a stronger foundation on which I could appreciate DeGroot's heavy dose of algebra and matter of fact presentation.

In short, I found this book to be "the exam," but not "the course."

48 of 54 people found the following review helpful:

5.0 out of 5 stars The best introductory manual to probabilities and statistics, 3 April 2001

By Daniel Ventosa S - Published on Amazon.com

This review is from: Probability and Statistics (Hardcover)

As a social science student (economics), statistics are crucial in a large array of topics of interest. During my studies, I have bought many probabilities and statistical manuals in order to understand the underlying theory I study (econometrics). So far, no one is better than this one (Mendelhall, Monfort among others). As someone said, probability is not an easy topic; sometimes it's pretty hard to understand particularly abstract concepts. Nevertheless, the author teaches you through an impressive quantity of examples. You don't need to be a genius in math's to understand him, because he explains pretty well. The equations are all understandable and the author's doesn't use I high level of sophistication to present complex problems. The content is pretty impressive; besides the classical probability theory (basic concepts, conditional probability, random variables, expectation...), there is an extensive section dealing with estimation techniques (maximum of likelihood, OLS, and Bayesian estimators) there is a chapter dealing with statistical tests and another with non-parametrical methods. The latter is somehow oldie and there are no explanations of the kernel density estimation or kernel regression. The other objection I can raise is that there are no explanations neither of sigma-algebras, a concept used in advanced probability. Of course, it's an introductory book, thus such drawbacks are understandable. This book should be in your library.

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