Believe it or not this important topic in applied statistics was developed by Hastie and Tibshirani when they were graduate students, a sure sign of greatness to come. After their graduation this highly acclaimed book came out explaining both the theory and pratice of generalized additive models in a clear and concise way. Generalized additive models are similar to generalized linear models in their additive structure but the form of the additive functions is much more general.

Both Hastie and Tibshirani are now Stanford professors in the Statistics Department and both have written other excellent books including their joint publication with Jerry Friedman "The Elements of Statistical Learning" and Tibshirani along with Efron wrote an excellent monograph on bootstrap.

A medical doctor recommended that I (a statistician) use generalized additive models on a project that he was involved with. We were pleased with the results of the modelling as they gave us some new insights on his topic and it was very rewarding

Because we were so impressed with the results, we both took the above book out of the library to read. I started the book with some trepidation as I feared it was going to be a complex topic and in some respects I was right. However, right from the encouraging quotes in the preface and into the text itself, I felt the authors were making a great effort to make the book readable.

The medico found that the explanations and examples were well written. His comment was that even a non-statistician could grasp what was going on but that there was enough theory that a statistician would be happy too!

This is realy the best book on the topic. In some sense it is the only modern book. If you work on Control Theory and you want to lern some modern geometric tecniques you have to read it !!!

This outstanding monograph relates optimal control theory to the calculus of variations. The field has been sorely lacking a comprehensive and unifying text to bring together mathematics' most challenging and important legacies. Finally, Velimir Jurdjevic has closed that gap, in an impossibly brilliant yet gripping and thorough series of proofs that promise to define the burden of future research. Velimir Jurdjevic, what can't he do?

This book is a good overview of how to implement in SAS some of the statistical methods you've probably already read about. I had done a lot of reading on factor analysis and clustering for my dissertation and needed a way to implement these methods in SAS. This book explains each method, then shows how to implement it using SAS. The data sets are from "The Handbook of Small Data Sets", which I got from my local university library. As I am a computer scientist and not a statistician, I found this book very helpful in putting together **the idea** of a particular statistical technique with the actual **implementation** of that technique. I have the first edition of this book also, and the second edition is much improved (especially the chapters on clustering and factor analysis).

Brian Everitt is the author of several very well-written statistical texts. Among them he has written a number that show how to implement statistical analyses usimg statistical software packages. This second edition of "A Handbook of Statistical Analyses using SAS" he has coauthored with Geoff Der.

As a SAS user, I find this book very handy along with other similar texts that I have on the use of SAS. What is particularly good about this book is that it serves as a guide to the use of various SAS procedures and also as an illustration of appropriate statistical approaches to real applications using SAS.

It starts out with a nice introduction to the SAS prrogramming language and its syntax and progresses through simple descriptive statistics to categorical data analysis to regression and analysis of variance and then on to more advanced topics, including survival analysis, logistic regression, generalized linear models,longitudinal data analysis, principle components, factor analysis and cluster analysis. Appendices provide SAS MACROs and SAS solutions to exercises in the text.

What is particularly good about this book, that may set it apart from some of the others, is the expert statistical advice about the implementation and interpretation of results in SAS. They provide excellent scholarly references to the statistical literature to support their advice. As an example, I particularly liked their discussion of Type I and Type III sum of squares in the analysis of variance. They give a clear explanation of what each means and when they are equivalent and when they are different. In addition, they present their own view as to which is the appropriate one to use in given situations and support their view with quotes from other researchers. Opposing positions are also mentioned and referenced.

Independent Component Analysis is a young and interesting topic that gained attention and still receiving more of it.

Until now this is the best introduction that has been written.
It is comprehensive, clear and unbiased.

I think that the book is a step toward making the subject not only a common field of research but also a reference for those looking for new challenging topics.

What worths mentioning is that the authors are very envolved in the development of the theory of ICA ,other books are good but are deviated by their author's own approachs and this is normal but unhealthy for a first book on any field.

What constitutes a great help for understanding ICA are the relatively easy concepts if one just intend to pick an algorithm(ex:FastICA), but this is not the case regarding its theory.

One colleague once argued that ICA should have emerged long before the begining of the 90's, claiming that Gaussian forms
(Central Limit-Theorem) killed the idea of dealing with other kinds of distributions and therefore the signal processing community went assuming every thing was gaussian (noise was gaussian,signals are gaussian),but the emerge of HOS relaxed the gaussian restriction and ICA became possible and no longer 'blind' .

I think this should prepare researchers to deal with coming challengs more intelligently and efficiently .That is why I recommend this book since it tries to give a broad view to the subject .

This is a nice and self-contained book on the subject of independent component analysis (ICA). The authors start with relevant mathematical and statistical background (in Part I) to prepare readers for the derivations of ICA (though seasoned researchers may want to skip the first part of this book). The authors discuss the motivation behind ICA and present several ways to derive ICA (since this subject has been approached by several communities). The authors also compare and discuss the pros and cons of these approaches. The authors discuss several applications using ICA in Part III.

Compared with other ICA books, this manuscript has much depth and completeness. I highly recommend this book to any reader interested in this topic.

This book contains the full development of a new kind of function theory that is closely connected with several interesting physical problems. This new theory is that generated by the metaharmonic generalized Dirac (Moisil-Theodorescu) operator, which factorizes the Helmholtz operator, and which leads to a very nice and powerful eight-dimensional generalization of classical complex analysis. The authors' approach is to stress the importance of integral representations to solve boundary value problems and then to apply these tools to handle some physical problems.

The contents are: Introduction and Some Remarks on Generalizations of Complex Analysis; Alpha-holomorphic Function Theory; Electrodynamical Models; Massive Spinor Fields; Hypercomplex Factorization, Systems of Non-linear Partial Differential Equations Generated by Fueter-type Operators; 4 appendices.

Intended mostly for researchers, but suitable for graduate-level courses. Includes lots of results found only in research papers, and some of them appear proved here for the first time.

Includes an extensive list of references. Just be careful with some typos.

If you are interested in hypercomplex analysis read also the books by Brackx et al, Delanghe et al, and Guerlebek & Sproessig.

Please take a look at the rest of my reviews. Thank you.

This is an important book whose title, unfortunately, reveals little of its contents. The subject is a rigorous formulation of classical electrodynamics and relativistic quantum mechanics in terms of complex quaternions. The latter form a mutidimenional algebra extending the complex numbers and possessing powerful generalizations of Cauchy's integral formulae. Although there are several books on the market using Clifford algebras (including complex quaternions) in mathematical physics, this book is distinguished in several ways. Most such books stop with the albegra, while the book under review also contains much rigorous analysis, including the "integral representations" of the title. Specialzing to complex quaternions (instead of considering general Clifford algebras) leaves ample freedom to describe the chosen physics while allowing the development of powerful new methods. The connections with classical results by Moisil, Theodorescu and others are emphasized, helping to motivate and orient the mathematical reader.

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

This book provides a detailed presentation of several important approaches to intelligent data analysis. It has ten chapters, each chapter written by a different technical specialist. The book could well serve as a text for a graduate level course on data analysis. It also works well as a reference. There are many useful illustrations and examples.

The first part of this book is focused on classical statistical issues. Arguably, anyone seeking to perform advanced data analysis should have a working knowledge of this area. It is my personal observation that, unfortunately, many workers do not. This book provides a good way of gaining a broad understanding of statistical methods. My only caveat is that the discussion of naïve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Naïve Bayesian classifiers have been reasonably successful in machine learning and a more in depth treatment would have been useful.

The later chapters focus on machine learning. They provide useful introductions into: induction, neural networks, fuzzy logic, and stochastic search. These chapters are particularly useful to workers contemplating how to best perform advanced analysis of complex, large, and possibly imprecise data sets. Consequently, someone contemplating data mining or other intelligent data analysis applications should seriously consider acquiring this book.

I am a relatively new student of stochastic processes. The first 4 chapters which are essentially devoted to explaining the theoretical concepts (Ito inetgration, semi-martingales, etc..) are so lucidly written that it is very easy for someone with a limited idea of stochastic process to comprehend them. After establishing these concepts, the book discusses how the earlier theory is applied to pricing by discussing the options pricing models under different scenarios. I felt that although i understood the earlier discussions regarding arbitrage, the part on how it relates with equivalent martingales was quite difficult to understand. But it is probably because i am new to this subject. In all this is an excellent effort by the authors to make a difficult topic readable and understandable. Finally i feel that people, who are genuinely interested in investing their hard earned money in options, should take the time and effort to learn from this book rather than the typical stereotype "How I Became a Millionaire Overnight Trading Options" books.

Introduction to option pricing theory.

Stochastic calculus.

Is difficult to find such a practical book in this area I THINK THAT THIS BOOK IS NOT ONLY THE BEST IF NOT THE ONLY ONE THAT FULFILL THE GENETICIST CALCULATION REQUIREMENTS! Congratulations to the author!!!!!!!!!!!!!

If to a Genetic Counselor the grammar of medical genetics i.e. risk factor calculation and implication of its outcome means everything then this book belongs to the best group of books. Being a student aspiring to be a genetic counselor, I found this book very informative and useful.Written in a simple way,the complex mathematical calculations have been described with the help of many easy to understand examples. All chapters bear the "key points" which divides the chapter into groups without breaking the continuity.After reading this book an immense idea of Statistical Genetics is bound to emerge in the mind of both students and professionals which will give confidence in the clinic. With best wishes to Ian D.Young for this precise & complete attempt in compiling a good reference book for the genetic counselors.

I'm a graduate student working in signal processing, and I have thus carefully read this book. It is very informative, and it contains most of the fundamental theory on the subject as well as more advanced results in additional complement sections. The book is very well written and is easily read. I warmly recommend it to any student in the field.

There are many books on spectral analysis and related topics. This book is particular suitable for the people who want to learn this topic by self-studying. The chapters are well-organized and up-to-date. If you have this book at hand, don't forget visiting the book's homepage (you can find the address in the book) and downloading some useful information, including lecture notes and MATLAB files. Those materials are really informative and helpful. I highly recommend this book for textbook in classroom or reference book for researchers.

This is one of the best books I've ever read in Stochastic Processes. Prof. Lawler presents Markov Chains (Finite, Countable and Continuous), Optimal Stopping, Martingales and Brownian motion concisely and straight to the gist of the subject. The exercises set at the end of each chapter fall into 2 categories: for people who read the book well and actually understand what has been stated, and to people who have a thorough understanding of solid probability theory (harder exercises).

Furthermore, it is such a small book that makes me wonder how so many information could fit in there.

The only small drawback is the few typos which can be picked up easily by the diligent reader.

In total is an extremelly good book, especially for people that haven't had an extensive contact w/ the subject before (or even measure theory), without losing any point of precision whatsoever.

In a very concise and clear way, this book gives readers all need to know in stochastic processes. It is very successful in approaching all problems and theorems without any measure theory. Great for students with all kinds of math background.