Thode does a masterful job of organizing the vast literature on testing normality. He has extensive bibliographies at the end of each chapter. He covers both univariate and multivariate tests. It is designed for practitioners with most of the theory avoided but all the references necessary to learn the theory are given. The title does not do the book justice. He includes outlier detection (univariate and multivariate) and testing for mixtures of normals. Even influence function measures that do not necessarily relate to the Gaussian model are discussed. To my pleasant surprise some of my work on the use of influence functions to detect multivariate outliers is mentioned briefly.

A nice feature of the book is the thoughtfully chosen quotes from statisticians at the beginning of each chapter. The first one provides the theme for the book. It is a quote from Egon Pearson in 1935 where Pearson (who with Neyman worked on optimal tests) says that it is permissible for statisticians to use less than efficient tests when simplicity or ease of computation are a consideration.

This text was commonly used as a graduate text in mathematical statistics in the 1970s when I was a graduate student at Stanford University. It was the best and most detailed text on the theory of hypothesis testing. Over the years it remained so and twenty years after publication, when it was outdated by research advances it was revised by Professor Lehmann. The second edition originally published by Wiley went out of print but has now been reprinted by Springer-Verlag. This is a great book for any statistician to have on his bookshelf, a must have reference!

Excellent, very useful mathematical boo

As the title suggests, Zhigljavsky has presented a much needed theory and analysis of stochastic global optimization methods. With roots in the Russian mathematical school, the work is thorough and exact. I have found parts of this work especially useful for formal work in evolutionary algorithms. The book contains a vast amount of detailed information on several topics of interest to mathematicians, statisticians, computer scientists and engineers. The only downside is one must be highly motivated to work through the results and notation. This is not an easily read book, but then which mathematically thorough work is.

I think bayesian theory will be used inevery fields of our life in the future.

This is a non-measure theoretic treatment of probability whose scope is similar to the first volume of Feller. Although each topic begins with a few simple but insightful examples, what they result is somewhat in-depth discussion. There are minor problems in writing and editing, but I found none so serious. This is another amazing Russian mathematics book that begins at an elementary level but ends with some real treatment, and this book is totally accessible to those who did not rigorously study analysis or measure theory. Highly recommend this to those who seriously desire to study probability.

What is also apparent as a feature of this book is that Gnedenko often notes the details of history. He spends a 70-page-appendix for this purpose besides many little footnotes. The author often integrates historical evidences as a part of the treatment. I believe one still benefits a lot from this historical notes, even if already familiar with the subject.

This book has chapters on the author's specialty topics: limit theorems and infinitely divisible distributions. This book also has a chapter on stochastic processes and elements of statistics, the latter which Feller's first volume does not touch. However, these brief treatments are only for introductions to each subject.

This is the best book I've found that takes a modern approach to statistical theory at the advanced undergrad or graduate level. Schervish covers the necessary probability background, classical and Bayesian decision theory, and hypothesis testing comprehensively, while maintaining a level of rigor suitable for math majors (the appendix nicely summarizes requisite results from measure theory and mathematical probability). It covers most if not all of the classical results found in Lehmann's TSH and TPE, but from a more modern perspective. This permits a more coherent, well organized presentation. The only (minor) drawback I found were some typos here and there -- to be expected from a first edition of a 600 pager. It was a joy to study from this book.

Horn and Johnson's MATRIX ANALYSIS AND TOPICS IN MATRIX ANALYSIS are true classics (like Knuth's Art of Computer Programming). You will find classic theorems and lemmas in matrix theory and linear algebra here along with their proofs (some of these are not found elsewhere).

TOPICS IN MATRIX ANALYSIS contains a lot of stuff including LMI's, Kronecker and Hadamard products of matrices and their properties etc. I found this book indispensible when I was studying Semidefinite Programming.

Both these books are now available in paperback (cost around 30+) dollars each. I have recently purchased both copies and can only strongly recommend them to anyone else.

I've been teaching from this book and its previous edition for 5 years. The format of this course is only 9 2-hour meetings for adult students who have not studied math for 20 years. It is so clear, and the examples are so relevant and complete, that the students are able to learn the material largely on their own. The problems are interesting, and there are plenty of them. The guided exercises are exceptionally clear and strongly encourage the students to get out a piece of paper and pencil and work along with the development of the solution. The book is cheerful without being silly. I tried other books, which were, for the most part, too heavy on formulas, too intense, and too light on intuition and reasoning. This one uses the standard formulas without turning off the mathematically anxious, by explaining the steps thoroughly. I would strongly recommend this book for introductory statistics for students who are mathematically weak, and I would also recommend it for stronger students, because there is no reason they should have to suffer.

I have taught statistics and research methods in two different depts of education, and over the years I have had the chance to review and try out many, many stat texts. No matter what text I've tried, I've seen students fumble, forget, and freak out as they slogged through the material. This book is the ONLY text I've ever used that ALL students found comprehensible and even, dare I say, interesting.

Of course, now it's out of print. =arg= Find it wherever you can. It is a rare gem.