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.

Diese Einführung beschreibt erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten, einheitlichen und anwendungsorientierten Form. Die Darstellung wendet sich an Studierende der Statistik in Wahl- und Hauptfach sowie an empirisch-statistisch und interdisziplinär arbeitende Wissenschaftler und Praktiker, zum Beispiel in Wirtschafts- und Sozialwissenschaften, Bioinformatik, Biostatistik, Ökonometrie und Epidemiologie. Die praktische Anwendung der vorgestellten Konzepte und Methoden wird anhand ausführlich vorgestellter Fallstudien demonstriert, um Lesern die Analyse eigener Fragestellungen zu ermöglichen.

Einf#x00FC;hrung.- Regressionsmodelle.- Lineare Regressionsmodelle.- Generalisierte lineare Modelle.- Kategoriale Regressionsmodelle.- Gemischte Modelle.- Nichtparametrische Regression.- Strukturiert-additive Regression.