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Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) (Hardcover)

Ludwig Fahrmeir (Author), Gerhard Tutz (Author)



Editorial Reviews
Review
From the reviews of the second edition:
TECHNOMETRICS
"A 25% size increase in a very generous effort for a new edition of a statistics book. If you own and like the 1E, then a purchase of the 2E would certainly seem appropriate. Anyone who deals with multivariate modeling should certainly purchase a copy. This book does not have a competitor for analyzing multivariate data with generalized linear models."
"The authors obviously put a great deal of work into this book … . There are nearly 40 examples … drawn from a variety of fields, extensively worked, and then reworked in succeeding chapters. … The vast amount of material is accurately presented … and laid out in an orderly and clear manner. … I conclude by endorsing this book whole-heartedly. Fahrmeir and Tutz have given the statistics community a wonderful resource for both teaching and reference." (Rick Chappell, Journal of the American Statistical Association, Vol. 98 (463), 2003)
"The 6 page subject index, the author index, the bibliography (updated considerably), and the nice LaTeX layout highlight the top quality we have come to expect from these authors and this publisher. … Statisticians everywhere will want to consult ‘Multivariate Modelling’, when confronted with multivariate data. Many scientists from the fields where examples originated will do so, too, and demand the application of the new and sophisticated procedures as described in the second edition. … Recommendation: buy." (Reinhard Vonthein, Metrika, December, 2003)
"This is an excellent book. Given the activity in the field, it substantially updates the material that is contained in the first edition and contains over 700 references. As well as providing references to work that is contained in the book, it makes ample suggestions for further reading of closely related topics. The result is a comprehensive book which provides an authoritative coverage of the subject area. … This book is a valuable edition to our library and is very highly recommended." (Paul Hewson, Journal of the Royal Statistical Society, Series A: Statistics in Society, Vol. 157 (3), 2004)
"This book brings together and reviews a large part of recent advances in the type of statistical modelling that are based on or related to generalized linear models. … Many real data examples from different fields illustrate the wide variety of applications of the methods. … The strength of this book is its extensive and thorough review by means of a unified notation and set of concepts of the basic ideas of the relevant literature. … The book is well written." (Jon Stene, Mathematical Reviews, Issue 2002 h)
"The aim of the new edition is to reflect the major new developments over the past years. The book is clearly written, with emphasis on basic ideas. The authors illustrate concepts with numerous examples, using real data from biological sciences, economics and social sciences. … this book gives a thorough exposition of recent developments in categorical data based on GLMs." (Oleksandr Kukush, Zentralblatt MATH, Vol. 980, 2002)
--This text refers to an alternate Hardcover edition.
Product Description
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of multivariate and multicategorical models within the generalized linear models framework. Based on well-chosen sets of data, these new developments are introduced to a not necessarily expert audience. Completeness was not an aim. The result is a self-contained, well-written text offering the applied researcher a useful insight into the applicability of the general linear model methodology." P.A.L. Embrechts, ETH-Zentrum, Zurich, Switzerland

Product Details Hardcover: 425 pages Publisher: Springer (May 1994) Language: English ISBN-10: 0387942335 ISBN-13: 978-0387942339

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关键词：Multivariate multivariat Statistical Generalized statistica certainly increase purchase reviews Series

Preface v
List of Examples xv
List of Figures xix
List of Tables xxii

1 Introduction 1

2 Modelling and analysis of cross-sectional data: a review of univariate generalized linear models 15

3 Models for multicategorical responses: multivariate extensions of generalized linear models 63

4 Selecting and checking models 119

5 Semi-and nonparametric approaches to regression analysis 151

6 Fixed parameter models for time series and longitudinal data 187

7 Random effects models 219

8 State space models 257

9 Survial models 305

Appendix A 345
Appendix B Software for fitting generalized linear models 367
References 379
Author Index 413
Subject Index 419

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).

[1] Studying bioinformatics? You must be familiar with multivariate analysis. This book is absolutely an important reference.
[2] A researcher of statistical pattern recognition? Without doubt, you need this up-to-date book to stuff your toolbox.

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).

楼主介绍得很详细，谢谢先

谢谢楼主分享！

不错，谢谢了！

谢谢！非常感谢！

thanks a lot ~~