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

Über die Autorin bzw. den Autor

Dr. Andreas Henking ist Gründer und Inhaber des Beratungsunternehmens RiskSIM und seit über 30 Jahren in den Bereichen Risikoanalyse, -controlling und -management tätig.

Dr. Christian Bluhm war Group Chief Risk Officer in der Konzernleitung der UBS mit insgesamt über 25 Jahren Erfahrung im Risikomanagement in Finanzinstituten.

Prof. Dr. Ludwig Fahrmeir war Professor für Statistik an der Universität Regensburg und der LMU München und ist der Statistik nach wie vor verbunden.

Karl Tasch ist Gründer und CEO des Softwareunternehmens FinAPU, das auf die Entwicklung und Bereitstellung von cloudbasierter Risikomanagement-Software spezialisiert ist.

Von der hinteren Coverseite

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.