In contrast to their graduate text "Time Series: Theory and Methods" this book is more elementary and introductory and is pitched at the advanced undergraduate level requiring only calculus, elementary statistics and matrix algebra. It gives very good coverage to a wide variety of time series models and includes some nonstationary models. In this second edition the chapter on nonstationary models includes the latest coverage of ARCH and GARCH models presented in a way that I found very accessible.

Computations are done with ITSM and in this edition the ITSM 2000 version 7.0 edition is included on a CD so that students can reproduce the authors' calculations and run analyses of their own.

Another nice feature of the text that distinguishes it from other texts at this level is the introduction of multivariate time series, coverage of state space models, chaos and cointegration. Ideas are illustrated with examples. Important theory is discussed but is kept brief and theorems and proofs are not given to the extent of their other more theoretical text.

Very good introductory book to ARMA models. Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book.

Very good introductory book to ARMA models. Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book.

I did an independent stats class after being out of math for a long time, the book was very helpful and I could actually figure the formulas. It was great!!

Very well written. Clear explanations of the subject matter with plenty of step by step examples. I highly recommend this book as it has significantly increased my understanding of statistics.

It was a great learning experience, with Itroductory Statistics by Weiss Neil A. It is easy to understand and Statistics has never been easier.

I came to this book with a need to become familiar with Kalman filters. I've read the first two chapters so far with great pleasure. Professor Kailath develops the material beautifully; his profound mastery of the field is evident in every paragraph. The material is concentrated, but is presented in a highly readable compelling style. The reader is expected to be comfortable with the basics of linear systems theory, probability, and matrix analysis, although extensive appendices provide the necessary background.

This is one of the best engineering textbooks I have read, period. Although the subject matter is not for the faint-hearted, the authors' attention to pedagogical details shine throughout (repetition is the key to learning). The Kalman filter is introduced naturally as a consequence of a general framework for obtaining the best linear estimator of a random variable given others (earlier observations), and the geometric intuition is stressed repeatedly.

No important issue is omitted, including a very complete treatment of numerical issues and fast algorithms. My only gripe is with the assumption that all model parameters are KNOWN; in other words, the important aspect system identification (parameter estimation, learning, or whatever you call it in your field) is left to other textbooks.

Moreover, and no minor accomplishment, is the amazingly small number of typographical errors (at least up to where I have read so far; a bit over half the book), which is remarkable given the dense mathematical contents.

All in all, I would give it 6 stars if possible. Everything is there: it transmits a deep intuition for the matter, a places it in its historical context through interesting and amusing notes; it leaves the reader fulfilled but not overwhelmed.

Kailath, Sayed, and Hassibi do an excellent job of explaining what is a fairly complicated subject. This book is best-suited for scholars who desire a deep understanding of estimation theory. Engineers who want to quickly understand how to implement a Kalman Filter might be better off buying Adaptive Filter Theory by Simon Haykin.

The first chapter provides a good overview of the book, although it makes the most sense once the subject matter of the rest of the book has been digested a bit. A consistent framework emphasizing innovations (or the new information which appears at any iteration) is used throughout the book, and both continuous and discrete-time techniques for stochastic estimation are given nearly equal treatment, although the real-world engineer is likely to be interested in the latter.

Professor Kailath's articulate nature and knack for the clever anecdote or one-liner shines throughout the book, making it, while very mathematical in nature, quite readable for the motivated student.

I have a graduate level physics and mathematics background and found that the real-world applications discussed in this book enhanced my understanding of Monte Carlo models. While I knew the math cold, this thought provoking book helped me make further model enhancements and allowances for the ambiguities in the models.

Monte Carlo experts who want to apply their knowlege to finance should also read: "Options, Futures, and Other Derivatives (5th Edition) by John Hull; and "Credit Derivatives" (2nd Edition) by Janet Tavakoli.

Gilks, Richardson and Spiegelhalter edited this marvelous collection of papers on applications of Markov Chain Monte Carlo methods. There has been a big payoff for Bayesians as this method has been a breakthrough for dealing with flexible prior distributions. Most (but not all) of the articles deal with Bayesian applications. The editors themselves start out with an introductory chapter that covers the basic ideas and sets the stage for the articles to come. They provide many references including several of the articles in this volume.

The list of authors is quite impressive and many interesting examples are presented. The editors themselves contribute to other chapters. Spiegelhalter and Gilks co-authored a chapter on a Hepatitis B case study with Best and Inskip. Gilks has a chapter on full conditional distributions and co-authors a chapter on strategies for improving the MCMC algorithms. Richardson contributes a chapter on measurement error.

George and McCulloch deal with the use of Gibbs sampling to choose variables in a model based on a Bayesian approach. Raftery also has a chapter on Bayesian approaches in hypothesis testing and model selection. Green covers image analysis. There are many others (25 chapters in all). This is a great reference for anyone interested in MCMC methods.

The BUGS (Bayesian inference Using Gibbs Sampling)software was developed by Spiegelhalter, Thomas, Best and Gilks to implement Gibbs sampling in a variety of contexts. They illustrate its use along with the diagnostic software CODA in the application in Chapter 2. It is also mentioned in various other chapters in the book. There is currently a version called winBUGS which is designed for Windows operating systems.

Before jumping into the use of MCMC a user would be well advised to study this book.

We recommend this book to anyone who is interested in learning MCMC methods. Contains a excellent selection of practical examples. Christopher Gordon and Steve Hirschowitz

This book must be a part of your library. I've read it several times, and highly recommend it.

The only review for this book is apparently by the author himself who says it's a great book. As he might be slightly biased in favor of his own writings a dissonant view is called for. Actually, he's right: this is an excellent book that brings together -- and explains as clearly as can do -- how to analyze statistically situations with discrete outcomes, determined by a large number of factors and where the interaction between those factors is potentially non-linear. A must-read for people who know how to run these methods on data in SAS but have no clue about the rationale behind them.

This is a really good book. I have read it several times.

DZ

I have already read several books on statistics and QC, but only few of them are so comprehensive as this one. It is helpful for both undergraduates and graduates students, who want to better understand the topics here presented. The book also comes with a data CD for exercices, thus making understanding easier for people.

Seldom can it be said that statistical methods are well explained. This Reference is a great melding of both method and application. I found that many of the concepts are structured in a way that allows for more practical use than theory explaination. Kudos to the authors.

The next millenium offers new opportunities for the application of statistics in industry. These opportunites come from better understanding of management needs and availability of wide spread computer power. The foreword by Bob Glavin, Chairman of Motorola Inc., provides the business motivation for the implementation of industrial statistics. The text presents modern graphical techniques, bootstrapping approaches and advanced SPC and DOE techniques. An unusual feature of the book is the availability of add-on software that allows the reader to practice hands-on learning with simulators and specially designed software for data analysis, acceptance sampling, SPC, design of experiments, and reliability. A useful book for students and practicioners alike.

In the 1970s this text became a classic on the subject of nonparametric methods. It was written for practitioners and students. It is introductory and comprehensive. It describes the methods accurately but does not cover the theory. Later Randles and Wolfe wrote a companion book covering the theory. This revision is much larger and covers the many advances over the past 20 years. It covers bootstrap methods as well. Also computational advances are discussed.

Conover's "Practical Nonparametric Statistics" is another fine book for practitioners. I also recommend Lehmann's book on nonparametrics. It was published in 1975 and is not easy to find these days.

This is an excellent book on a somewhat underutilized group of statistical techniques. It could be used for a course in nonparametric statistics at the graduate level in Psychology or the social sciences, although I don't think the whole book could be covered in a semester.

It is perhaps more valuable as a reference for the practicing data analyst. Because of the format, it is relatively easy to find a procedure that does what you want. There are 11 chapters, the first of which is an introduction, and the others each cover one type of problem (e.g. the one-sample location problem). Within each chapter are a variety of procedures, each of which is discussed in the same format: Procedure, large-sample approximation, ties, example, comments, properties and problems.

In addition, there are close to 200 pages of tables, many of which I haven't seen elsewhere.

Overall, highly recommended for anyone who needs to use or teach these techniques.

I found this book to be very helpful and it required minimal interpretation from academia to understand. More so for the practicioner than the theoretician.

As a former graduate school student of Prof. Hoffman (who took his graduate level class on numerical methods) I would only expect nothing but excellence from one of his publications. After 15 years, I continue to regularly use many of the methods presented by Prof. Hoffman in my daily engineering work.

Dr. Hoffman has written a splendid book. For an introduction to numerical methods it is lucid and in-depth simultaneously. Provides fundamentally important knowledge on the whole gamut of numerical solutions techniques and issues.

the best book for numerical methods which i feel personally. the topics on taylors series was very interesting.

the numerical integration and other topics were really enlightening

I have been teaching statistical methods and epidemiology for graduate students in the health area for more than 5 years and this book is a hit. Previously I tried Armitage & Berry and got a lot of resistance from the students. Altman's book is well organized, presents the problems and their solutions in a very intuitive way, and focus on the real problems in the area. Very good for introductory courses. I usually use Kirkwood's Essentials of Medical Statistics in parallel.

This is a very well written and popular text on biostatistics. Altman writes for non-statisticians but the book is best suited for those with at least one prior course in statistics and those who have had mathematics through high school algebra. Emphasis is placed on the important practical problems. Good statistical designs and analyses are emphasized. The pitfalls with many published medical articles are discussed in Chapter 16.

I used this book to teach a 20 lecture course to students (engineers, clinicians and computer scientists) at Pacesetter in 1998 and at Biosense Webster in 1999 (both medical device companies that employed me as senior biostatistician). It was a good refresher course for the CRAs and engineers and it helped to make it easier for me to work with them on their statistical problems.

I have also taught a similar course to undergraduate students in the Health Science Department at Cal State Long Beach. Altman's book is a little too advanced to use as a text for that course but I did use it as a reference and covered material in Chapter 16 at the end of the course. Clear discussion of the medical literature is very important to these students and Altman does a great job!

Doglas Altman's book is extremely useful for Statisticians involved in giving consultancy to non-Statisticians. I would almost say that it gives too many secrets away! Practically the whole field of Medical Statistics is covered. There is a specially good section on power calculations for clinical trials; the nomogram and examples should save a lot of time that might otherwise be spent in using formulae. Having said that, there is software around for this sort of calculation. The book contains much valuable advice on real-life advice Statistical issues, and and realistic problems (with answers) are provided for practice. kvery valuable.

The book is great but the notations the authors employ will make you want to drop it on a first reading. Despite the generic title, it is really a reference book for the experts.

Issues in generalization are presented better in the book by Anthony and Bartlett but overall it is the best book available (for learning theorists).

This book provides a solid theoretical foundation for pattern recognition and statistical learning. If you consider yourself and expert, or want to be an expert in this field, this book is a must read. It will make you think hard about the concepts (and may be question whether you are or want to become an expert!).

This is an awesome book, the best in-depth book on statistical classification to date. Filled with theorems and proofs on classical nonparametric techniques plus neural networks and learning. Standard reference for anybody doing serious pattern recognition and learning. Destined to become a classical reference in the field.