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.
