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

Fahrmeir, Ludwig; Tutz, Gerhard

Hardcover

ISBN 10: 0387951873 ISBN 13: 9780387951874

Verlag: Springer, 2001

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

Ludwig Fahrmeir is Professor Emeritus at the Institute of Statistics at LMU Munich, Germany. From 1995 to 2006 he was the speaker of the Collaborative Research Center 'Statistical Analysis of Discrete Structures', supported financially by the German National Science Foundation. His main research interests include semiparametric regression, longitudinal data analysis and spatial statistics, with applications ranging from social science and risk management to public health and neuroscience. Thomas Kneib is a Professor of Statistics at the University of Göttingen, Germany, where he is the Speaker of the interdisciplinary Centre for Statistics and Vice-Speaker of the Campus Institute Data Science. He received his PhD in Statistics at LMU Munich and, during his PostDoc phase, was Visiting Professor of Applied Statistics at the University of Ulm and Substitute Professor of Statistics at the University of Göttingen. From 2009 until 2011 he was Professor of Applied Statistics at Carl von Ossietzky University Oldenburg. His main research interests include semiparametric regression, spatial statistics and distributional regression. Stefan Lang is a Professor of Applied Statistics at the University of Innsbruck, Austria. He received his PhD at LMU Munich. From 2005 to 2006 he was Professor of Statistics at the University of Leipzig. He is currently Associate Editor of the journal Statistical Modelling. His main research interests include semiparametric and spatial regression, multilevel modelling and complex Bayesian models, with applications, among others, in development economics, environmetrics, marketing science, real estate and actuarial science. Brian D. Marx was Professor at the Department of Experimental Statistics at Louisiana State University, LA, USA. He passed away shortly after the authors finished the work on this 2nd edition. His main research interests included P-spline smoothing, ill-conditioned regression problems, and high-dimensional chemometric applications. He was serving as Coordinating Editor for the journal Statistical Modelling for many years, was Chair of the Statistical Modelling Society, and a Fellow of the American Statistical Association.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2001
• Sprache: Englisch
• ISBN 10: 0387951873
• ISBN 13: 9780387951874
• Einband: Gebundene Ausgabe
• Auflage: 2
• Anzahl der Seiten: 544
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