Regression

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Now in its second edition, this textbook offers a comprehensive introduction to parametric, nonparametric, and semiparametric regression, bridging the gap between theory and application. Key models and methods are presented with a solid formal foundation, illustrated through numerous examples and case studies. Important definitions and statements are summarized in boxes, and the underlying data sets and code are accessible online. The selection of methods emphasizes the availability of user-friendly software. Topics covered include classical linear models, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression, and distributional regression models. Two appendices provide essential matrix algebra, probability calculus, and statistical inference. This revised edition expands on regression models, incorporating the relationship between regression and machine learning, enhancing details on statistical inference in structured additive regression, and offering a reworked chapter on quantile and distributional regression models. Regularization approaches are discussed more thoroughly throughout. The book targets students, educators, and practitioners in social, economic, and life sciences, as well as those in statistics, mathematics, and computer science interested in statistical modeling and data analysis, and is written at an intermediate