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.

Probably the best in its class, Dr. Collet's work is among those books that contain a great deal of information and clarity. It deals with model building with binary data. A vey good explanation on the rationale behind maximum likelihood aproach to parameter estimation, it also shows the application of the asimptotic properties of the most commonnly used models for cross tabulated data.
I hold a Ph.D. in agronomy and plant breeding, and this volume certainly gave me a good start for analyzing binary responses. Lots of examples help in understanding the theory. A 5 stars without any doubt.

This is an excellent volume on how one should tackle binary data. Whether you have previous experience on this type of data or not, one can learn a lot on the subject with this book.

It was written in an easily understood way so that one can really follow the examples and have a real go at one's own data while consulting the book.

As a former student of Prof. Collett I recall how clear his presentation as well as lecture notes had always been on even the most complicated subject he taught. I treaure this volume very much.

This book teaches us a lot of methods in multivariate data analysis and the use of a model-building paradigm makes it easy to learn multivariate data analysis

A very informative and useful handbook for both practitioners and students. Each section provides a glossary, the statistic's theoretical assumptions, a step-by-step outline, a sample article that uses the statistics, and most importantly, the command code for SPSS and SAS. A must for anyone doing advanced level statistics!

Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.

I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.

The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.

Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.

Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).

Great book! Presents clear information about statistical computational details, as well as a number of nonstandard models (including those of Tutz's original work). The book has a transparent build-up, from more easy modeling exercises to advanced applications. I like best the part on generalized linear time series modeling, using the extended Kalman filter in the context of the EM algorithm. The only critique I have concerns the handling of (the variance of) the measurement error term in the associated generalized state space model (this measurement error should be modeled as a constrained martingale difference).

I have recently reviewed for amazon the texts by Gallant and the one by Bates and Watts. This text was written by Seber and Wild, two accomplished statisticians and experienced authors. This volume is of the same high caliber as those texts and deserves mention. It is a longer text that overlaps on many topics with the other two books, deliberately neglects some areas that were well covered by Gallant (Gallant's book came out in 1987 and this one in 1989) and hits some topics not covered by either of the other two books.

Bootstrap methods are neglected probably because the value of the bootstrap for standard error estimation in nonlinear models was not yet appreciated in 1989.

Chapters 1 and 2 provide good introductory material similar to the other texts. Chapter 1 deals with the models (linear and nonlinear) and Chapter 2 provides the basic estimation techniques. In addition to the standard material on least squares, generalized least squares and maximum likelihood, the authors also cover quasi-likelihood, linear approximations, robust estimation and Bayesian methods. Box - Cox transformations and the issue of variance heterogeneity are also treated in Chapter 2.

As they remark in the preface, they avoid much of the econometric theory and asymptotic theory that is well covered in Gallant's book.

Chapter 3 deals with important practical issues including the convergence properties of the iterative procedures (important for nonlinear models but a non-issue in linear models), ill-conditioning and identifiability (important issues for both linear and nonlinear models).

Chapter 4 deals with curvature issues and covers much of the original work of Bates and Watts with many references to those authors. Oddly though, there is no mention of the Bates and Watts text. Both books were published by Wiley around the same time with Bates and Watts appearing in 1988 and Seber and Wild in 1989. Perhaps the Seber and Wild book went to the publisher before the Bates and Watts book came out (their preface has a May 1988 date).

Important and interesting topics covered in this book but not the others include models with time dependent errors, detailed treatment of growth models, compartmental models, multiphase and spline regresions and error-in-variables models. They also devote a whole chapter to software issues (very interesting and practical but probably mostly outdated).

Good for a graduate statistics course or for a research reference source. Has lots of material and references but lacks homework problems.

This book covers the whole theory of nonlinear regression. I think it is essential both for students of statistics and for scientists, not only as a study book but also as a reference book. I recommend it to those who already have had an introductory course on the subject and need to go deeper into it.

The latest edition of Dr Grimmett's Percolation is surely the best book on the subject. He presents topics as clearly as possible without neglecting the technical details. His writing style is very readable, making much of this book accessible even to those who don't have all the necessary background in mathematics to understand all the proofs. Anyone looking for an easy introduction to the topic would be better off with Stauffer's book. But to gain any moderate understanding of this fascinating subject, and the methods and results of current research, this is the only book to have.

Grimmett's book, Percolation, is excellent.

Percolation theory began in the 50's; its mathematics is now quite mature, but the theory has recently acquired new techniques because many of the questions initially raised by percolation theory are still unanswered.

Percolation technology is now a cornerstone of the theory of disordered systems, and the methods of this book are now being extended into dynamical systems theory and the life sciences. This book covers the mathematics of percolation theory, presenting the shortest rigorous proofs of the main facts. Many problems in percolation theory are beautiful, but some of the apparent simplicity of the subject is deceiving, because the subject is quite deep. Grimmett cuts through many of the difficulties presenting the important concepts clearly and sucinctly.

The author restricts himself- for accessibility to the maximum readership-to bond percolation on a cubic lattice. Grimmett presents the core material at a graduate level for folks conversant with elementary probability theory and real analysis. Having some knowledge of ergodic theory, graph theory, and some mathematical physics helps, however. There is litle discussion of continuous, mixed, inhomogenous, long range, first passage or oriented percolation.

Beginning with existance of Psubc for the edge probability p we arrive at an infinite open cluster followed by discusssion of the basic techniques of the FKC, BK inequalities and Russo's formula. Grimmett then discusses open clusters per vertex and subcritical percolation, beginning with the Aizeman-Barsky and Menshikov methods for identifying the critical point, followed by a systematic study of the subcritical phase. He then discusses supercritical percolation, including 2 dimensional percolation, continuum percolation and random processes. The author gives a full list of references.

Professor Isaac has written a book for those interested in learning about probability. It is at a high school algebra level although knowledge of calculus could be helpful at times. He starts with the now famous Monte Hall problem and provides the most lucid explanation I have seen to date. This is a great way to introduce important probability notions such as sample space and probability models for the sample outcomes. Deals mainly with discrete probability which is easiest to understand and yet rich with applications in gambling and other areas.

Important theory is presented but without the detailed mathematical proofs. Covers the gambler's ruin, geometric probability, Monte Carlo methods and some statistical decision theory. He also presents both the frequentist (throughout the text)and the Bayesian paradigms (Chapter 4) for statistical inference. Examples of the application of probability to statistical inference is nicely treated in Chapter 15. The deeper material on Markov chains and Brownian motion are relegated to the last two chapters (16 and 17). The exposition is excellent throughout and many good references are provided for readers who want to learn more or delve deeper into the theory.

This introduction to probability and statistics teaches you about important concepts, theorems and applications without going into proving most of them. It's easily accessible to amateur mathematicians with a bit of persistance, and it illuminates many of its concepts using famous problems. I'm going to take a statistics course next year, and I found this to be a delightful introduction to the topic.