© 2001

Multivariate Statistical Modelling Based on Generalized Linear Models


Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Ludwig Fahrmeir, Gerhard Tutz
    Pages 1-14
  3. Ludwig Fahrmeir, Gerhard Tutz
    Pages 139-172
  4. Ludwig Fahrmeir, Gerhard Tutz
    Pages 173-240
  5. Ludwig Fahrmeir, Gerhard Tutz
    Pages 241-281
  6. Ludwig Fahrmeir, Gerhard Tutz
    Pages 283-329
  7. Ludwig Fahrmeir, Gerhard Tutz
    Pages 331-383
  8. Ludwig Fahrmeir, Gerhard Tutz
    Pages 385-431
  9. Back Matter
    Pages 433-518

About this book


Since our first edition of this book, many developments in statistical mod­ elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv­ ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.


Fitting Generalized linear model Regression analysis Survival analysis Time series best fit data analysis expectation–maximization algorithm

Authors and affiliations

  1. 1.Department of StatisticsUniversity of MunichMünchenGermany
  2. 2.Department of StatisticsUniversity of MunichMünchenGermany

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From the reviews of the second edition:


"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)