Now in its second edition this textbook provides an applied and unified introduction to
parametric nonparametric and semiparametric regression that closes the gap between theory and
application. The most important models and methods in regression are presented on a solid
formal basis and their appropriate application is shown through numerous examples and case
studies. The most important definitions and statements are concisely summarized in boxes and
the underlying data sets and code are available online on the book's dedicated website.
Availability of (user-friendly) software has been a major criterion for the methods selected
and presented. The chapters address the classical linear model and its extensions generalized
linear models categorical regression models mixed models nonparametric regression
structured additive regression quantile regression and distributional regression models. Two
appendices describe the required matrix algebra as well as elements of probability calculus
and statistical inference.In this substantially revised and updated new edition the overview on
regression models has been extended and now includes the relation between regression models
and machine learning additional details on statistical inference in structured additive
regression models have been added and a completely reworked chapter augments the presentation
of quantile regression with a comprehensive introduction to distributional regression models.
Regularization approaches are now more extensively discussed in most chapters of the book. The
book primarily targets an audience that includes students teachers and practitioners in social
economic and life sciences as well as students and teachers in statistics programs and
mathematicians and computer scientists with interests in statistical modeling and data
analysis. It is written at an intermediate mathematical level and assumes only knowledge of
basic probability calculus matrix algebra and statistics.
