I have done masters degree in MATHEMATICS with statistics optional and have done some management courses also. Here I am a PROFESSOR in a COLLEGE where students do B.SC. from LONDON university under EXTERNAL PROGRAM. One of the subjects I teach is "Elements of statistics". we prepared from this book,new sections were written in very simple way with technical examples and with graphs. My students last year got the DISTINCTION in this subject and this year I am also expecting DISTINCTION again from students. So I wanted to contact the auther and compliment the person. Finally as a teacher I would recommend strongly this book for the students of BSC. for "Elements of Statistics". this is a must read for the said subject.

This is a great book for beginners because it's very well written and has easy-to-follow examples. If you're familiar with stats and only need something for reference, you'll find that this book is comprehensive, and has a great index.

This book provides a very readable introduction to basic statistical analysis using R (with occational references to S-Plus). The table of contents displays the topics and I thought they were generally well covered in enough detail to compute the statistics (but this is not a statistics text). Especially helpful are the additional analysis steps, such as graphing results, and the peripheral R issues. Small things I would change: expanded coverage of manipulating data (e.g., SPSS's RECODE, TEMPORARY, MERGE FILE,...), more explicit instructions on installing the example data (it's at the end of the installation Appendix), discussion of interactions in ANOVA and regression, discussion of ANCOVA, and finally I would have liked a quick overview of the available packages and the stats they provide. But these are small issues; it's a great book.

Introductory Statistics with R is an important book for a rapidly developing field. R is an extremely powerful statistical computing environment which suffers from the same problem as almost every other free software project -- a lack of quality documentation. Dalgaard fills a major gap with this book, that is, a guide to using R for many standard statistical problems.

For some time now, users have had to make do with S-PLUS books which contained some overlap with R. Now R users have a book they can call their own. After briefly discussing the R system and the language basics, Dalgaard goes through what might be covered in an advanced undergraduate data analysis course. Throughout the book, code examples and output are carefully interspersed so that the reader doesn't go too long without having a concrete example.

Dalgaard leaves out some advanced topics such as time series, spatial statistics, etc. (some of which are nicely covered in Modern Applied Statistics with S by Venables and Ripley) but that is probably for the best. The book is not bloated, nicely priced and I would recommend it to any advanced undergrad or first year grad student wanting to learn how to do statistical analysis in R.

K-theory is a challenging subject; Kasparov's KK-theory even more so. But Bruce Blackadar's textbook manages to present both of these in a clear and coherent fashion, readable both by mathematicians and physicists. The ideal reader of this book will already be familiar with the basics of operator algebras and will have had some exposure to K-theory (perhaps through Wegge-Olsen's remarkable book) and noncommutative geometry; but an extensive background is not needed.

One especially good feature of this book is the wealth of examples it contains, especially those examples of most relevance to string theory. (Where K-theory is finding some of its most interesting applications today) A wide range of subjects such as "topological" K-theory (the K-theory of gauge fields) and supersymmetry (referred to as "grading," following mathematicians' notation) round out this presentation of one of the most exciting subjects on the border of mathematics and physics.

The books covers not only K-theory but also KK-theory that was introduced by G. G. Kasparov. The original article by Kasparov are very hard to read because of the generality of his approach. Furthermore the theory was much simplified by Skandalis et al. The second part of the book offers a very readable introduction to KK-theory. Now the new edition even contains a short exposition of E-theory.

"Elements of KK-theory" by K. K. Jensen and K. Thomsen is good introduction too.

I agree with the brief comments of the other Amazon reviewer. However, these authors have updated the material in a book just published in 2000 "Linear Mixed Models for Longitudinal Data". The approach is the same in both books but the new one contains a lot of new advances that have occurred over the last three years. If you want a thorough account with the latest developments buy the other book. I have given a thorough review of the other book for Amazon. On the other hand if you just want to learn longitudinal data analysis this monograph will work for you and may be less expensive since it is in paperback while the other book is only currently in hardcover. Both books illustrate examples using SAS Proc Mixed.

Linear mixed model has been widely used in biomedical reserach, such as longitudinal observational studies and clinical trials. However, the model is theoretically complicated, and is hard to use it. This book not only covers statistical methodologies related to this model, but also gives excellent examples which can teach the readers how to use it.

Princeton University Press originally published this book in the mid 1940s. At the time, Harold Cramer was a leading statistician in Sweden. The field of mathematical statistics was just being formalized and there were no existing books with good mathematical treatment of inference methods. Kolmogorov was developing a rigorous measure theoretic basis for probability while Fisher and Neyman were developing different schools of thought on statistical inference.

Cramer put together the standard reference on mathematical statistics which is still valuable today. This is the type of book to be enjoyed by mathematical statisticians looking at it from a historical perspective. Recent advances require those who need a modern course to study other texts.

This was the first textbook on modern mathematical statistics, and is still one of the best. This work was one of the key elements which transformed probability theory and statistics into rigorous and powerful branches of mathematics.

It begins with an introduction to the theory of integration and measures, assuming no more than a working knowledge of calculus, and is one of the best introductory expositions of measure theory available.

The second part of the book is on statistical inference, and follows the three giants of modern statistics, Fisher, Neyman and Pearson. Cramér explains the difficult subjects of confidence regions and Neyman-Pearson hypothesis testing clearly and convincingly. The closing portion of the book discuss analysis of variance and linear regression methods; and is supplemented with real-world examples throughout, leaning heavily on the data provided by the Swedish census.

This book is a classic, not least for its combination of lucidity and rigor. In recognition of its merits, it has been re-issued in an affordable paperback edition. It belongs on the shelf of anyone interested in statistical methods.

This is a pretty technical book on theoretical statistics based on measure-theory. It's very well written. For Ph.D. students or readers with experience in analysis/measure-theory, it's a good investment. For less technical book, I would recommend Casella and Berger's Statistical Inference.

This book has all the ingredients of what in my opinion constitutes an excellent mathematics text: rigorous, concise,
self-contained, clear, and taking an abstract point of view. Note however that, due to the latter ingredient, the author studies statistics using a measure-theoretic approach; and thus I highly recommend that a potential reader first study measure theory as a prerequisite. The first chapter reviews the basics of measure theory, but it may seem too giant a first step for some readers.

The first two chapters of the book give a nice overview of probability and statistics, while the remaining chapters expand on three fundamental areas of statistical inference: estimation (both parametric and nonparametric), hypothesis tests, and confidence sets). And I must admit that I'm very impressed with the author! For if a textbook is a reflection of what an author knows about some subject, then Shao represents a treasure trove of knowledge that is so eloquently shared in this book. Anyone serious about doing graduate-level reasearch in statistics should invest a year of studying this book. But be forwarned that most likely one will find this, due to the onslaught of measure theoretic analysis, one of the more challenging books to makes its way on the book shelf. For those who cannot stomach so much analysis, but would like to at least understand the gist of statistics, I recommend Roussas's book of the same title. It is calculus-based and makes some simplifying assumptions (e.g. continuous or discrete) about the distributions, which helps make the math digest easier.

I am not a statistician, but this book has been my major reference on matrix algebra since I got it. The presentation is a bit dense, but I want to point out that the author actually presents the proofs to essentially _all_ theorems in the book. Perhaps this explains the style. As for the content, I find this book very comprehensive in my experience. But the dense page-setting of the book actually makes it visually challenging to locate a result. I also note that there are extensive exercises at the end of every chapter, although I probably won't use this as a textbook for my students.

This book is a true rarity. The exposition is very mathematical, and, therefore, many mathematicians (interested in matrix algebra) will find this book very useful and interesting. The exposition is clear, and quite complete. Of great interest are topics such as idempotent matrices, differentiation of matrices, invertibility.

Once one has read the articles in this book it becomes clear that meta-heuristics are intelligently designed methods to provide decision makers with tools for decision support. Each chapter is self-contained and provides different insights into specific methods (especially genetic algorithms, neural networks, tabu search, simulated annealing). These methods are well explored and explained by means of theoretical as well as practical results, e.g., for vehicle routing or mail delivery or generalized assignment. Ideas on how to implement the methods are also provided. Most papers are easy to read with only some preliminaries in mathematics (or combinatorial optimization). The chapters are carefully collected and could have been accepted for high-quality journals as well. Well done.

i have no idea what it is avbout, but it looks really cool from the name and totally confusing nature of it.

Burnham and Anderson have put together a scholarly account of the developments in model selection techniques from the information theoretic viewpoint. This is an important practical subject. As computer algorithms become more and more available for fitting models and data mining and exploratory analysis become more popular and used more by novices, problems with overfitting models will again raise their ugly heads. This has been an issue for statisticians for decades. But the problems and the art of model selection has not been commonly covered in elementary courses on statistics and regression. George Box puts proper emphasis on the iterative nature of model selection and the importance of applying the principle of parismony in many of his books. Classic texts on regression like Draper and Smith point out the pitfalls of goodness of ift measures like R-square and explain Mallows Cp and adjusted R-square. There are now also a few good books devoted to model selection including the book by McQuarrie and Tsai (that I recently reviewed for Amazon) and the Chapman and Hall monograph by A. J. Miller.

Burnham and Anderson address all these issues and provide the best coverage to date on bootstrap and cross-validation approaches. They also are careful in their historical account and in putting together some coherence to the scattered literature. They are thorough in their references to the literature. Their theme is the information theoretic measures based on the Kullback-Liebler distance measure. The breakthrough in this theory came from Akaike in the 1970s and improvements and refinement came later. The authors provide the theory, but more importantly, they provide many real examples to illustrate the problems and show how the methods work.

They also refer to the recent work in Bayesian methods. Chapter 1 is a great introduction that everyone should read. Being a fan of the bootstrap I was interested in their coverage of it in chapters 4, 5 and 6 (much of which is the authors' own work).

Because the authors work in biological fields they cover survival models as well as the standard time series and regression models where most of the emphasis has been placed on model selection in the past.

It is a great reference source and an important book for learning about model selection as part of the inferential process. The pictures of the famous contributors inserted throughout the book is also nice to see. We have Akaike, Boltzmann, Shibata, Kullback, and Liebler brought to life in photographs or sketches.

Statistical data analysis usually goes through cycles of exploring and looking for patterns in data, often through model construction, analyzing residuals and modifying model fits, until all unusual features being explained. Though this practice has been going on for more than 100 years, it has not been closely examined to see whether the fact that your analysis based on the best fitted model using the same data set should be biased, or plainly you cheated by over-analyzing your data. This book by the two productive authors say yes, and you should rethink about what you have been doing. A highly applaudable and timely efforts on the part of the authors, considering that the trend of over-analyzing your data is increasing rapidly with recent explosion of data and intensive computer analysis in the data mining industry. It's not as hopeless or bad as you think, and there are ways to avoid pitfalls and there may exist ways of making some valid inference out of this model selection process. So enjoy reading this book and think!

The best thing about this book is that it combines financial time series analysis with "real-life" examples that are either reproducible or easily adaptable. Being that it is also the user manual for the S+FinMetrics module for the SPLUS stats. package it also reads like a software manual (some people like that). This book provides a good sample of many time series techniques that can be applied out of the box.
Note: This book comes with the S+FinMetrics module.

This is an excellent book on financial econometrics, very practical yet rigorous. I wish all econometrics/statistics textbook could like this. Basic theory followed by practical examples - real life examples, not simplified ones like in other books. The authors gave detailed instructions on how to implement various econometric models, i.e. multi-factor models, GARCH, MGARCH, long memory models, state-space, etc. Most econometrics textbooks are at two extremes, they are either too theoretical (you still don't know how to put those models in real life), or too simple (lack of mathematical rigor and without advanced applications). This book is a combination of both worlds, computer codes/math models, and real life examples (some really good ones). A lot of cutting-edge techniques and advanced topics are also covered.