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Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) | 
 enlarge | Authors: Ludwig Fahrmeir, Gerhard Tutz
Creator: W. Hennevogl
Publisher: Springer
Category: Book
List Price: $99.00
Buy New: $55.00
You Save: $44.00 (44%)
New (19) Used (7) from $55.00
Rating:  4 reviews
Sales Rank: 914188
Media: Hardcover
Edition: 2nd
Pages: 548
Number Of Items: 1
Shipping Weight (lbs): 2
Dimensions (in): 9.2 x 6.5 x 1.4
ISBN: 0387951873
Dewey Decimal Number: 519.538
EAN: 9780387951874
Publication Date: April 20, 2001
Availability: Usually ships in 1-2 business days
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Product Description
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. It deals with regression analysis in a wide sense to include not only cross-sectional analysis but also time series and longitudinal data. The authors 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. After a review of generalized linear models, 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.
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| Customer Reviews:
Absolutely an excellent work. Don't hesitate to pay for it! September 21, 2005
supercutepig (USA)
8 out of 8 found this review helpful
[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.
A quality text April 3, 2001
prof.dr. P.C.M. Molenaar (amsterdam Netherlands)
6 out of 6 found this review helpful
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).
multivariate methods using generalized linear models May 11, 2001
Michael R. Chernick (Malvern, PA)
11 out of 14 found this review helpful
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).
 nice theory on multivariate generalized linear models January 24, 2008
Michael R. Chernick (Holland PA)
24 out of 24 found this review helpful
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).
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