Ludwig Fahrmeir is Professor emeritus at the Department of Statistics at Ludwig-Maximilians-University Munich. From 1995 to 2006 he was speaker of the Collaborative Research Center 'Statistical Analysis of Discrete Data', supported financially by the German National Science Foundation. His main research interests are 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 Professor for Statistics at Georg August University Gottingen, Germany, where he is speaker of the interdisciplinary Centre for Statistics and a Research Training Group on "Scaling Problems in Statistics". He received his PhD in Statistics at Ludwig-Maximilians-University Munich and, during his PostDoc phase, has been Visiting Professor for Applied Statistics at the University of Ulm and Substitute Professor for Statistics at Georg-August-University Gottingen. From 2009 until 2011 he has been Professor for Applied Statistics at Carl von Ossietzky University Oldenburg. His main research interests include semiparametric regression, spatial statistics and quantile regression. Stefan Lang is Professor for Applied Statistics at University of Innsbruck, Austria. He received his PhD at Ludwig-Maximilians-University Munich. From 2005 to 2006 he has been Professor for Statistics at University of Leipzig. He is currently editor of Advances of Statistical Analysis and Associate Editor of Statistical Modelling. His main research interests include semiparametric and spatial regression, multilevel modelling and complex Bayesian models, with applications among others in environmetrics, marketing science, real estate and actuarial science. Brian D. Marx is a full professor in the Department of Experimental Statisitics at Louisiana State University. His main research interests include P-spline smoothiing, ill-conditioned regression problems, and high-dimensional chemometric applications. He is currently serving as coordinating editor for the journal Statistical Modelling and is past chair of the Statistical Modelling Society.

• Introduction.- Regression Models.- The Classical Linear Model.- Extensions of the Classical Linear Model.- Generalized Linear Models.- Categorical Regression Models.- Mixed Models.- Nonparametric Regression.- Structured Additive Regression.- Quantile Regression.- A Matrix Algebra.- B Probability Calculus and Statistical Inference.- Bibliography.- Index.

From the book reviews: "This is a very useful book for researchers, in particular those often faced with data not suited to the classical linear model, and for teachers who wish to motivate good students with an introduction to the wonderful and diverse world of modern statistical modeling. The use of interesting examples and well-thought-out remarks, together with important theory, aid the reader in getting a very good feel for the topics covered." (Luke A. Prendergast, Mathematical Reviews, June, 2014) "The book is an excellent resource for a wide range of readers ... . more accessible to readers interested in applications of these procedures. ... Summing Up: Highly recommended. Students of all levels, researchers/faculty, and professionals." (D. J. Gougeon, Choice, Vol. 51 (8), April, 2014) "This is a comprehensive review of various types of theoretical and applied regression models and methodology. ... The book provides a strong mathematical base for the understanding of various types of regression models and methodology by integrating theory and practical application. ... This is an excellent reference for teachers, students, and researchers in statistics, mathematics, and social, economic, and life sciences." (Kamesh Sivagnanam, Doody's Book Reviews, August, 2013)