Regression: Models, Methods and Applications

This textbook offers a comprehensive and unified introduction to parametric, nonparametric, and semiparametric regression, covering various models and methods with examples and case studies. It is written for students, teachers, practitioners, and researchers in social, economic, life sciences, statistics, and mathematics. The second edition includes extended coverage of regression models, machine learning, statistical inference, and regularization approaches.

Format: Paperback / softback
Length: 746 pages
Publication date: 17 March 2023
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

This comprehensive textbook, now in its second edition, offers an applied and unified introduction to parametric, nonparametric, and semiparametric regression, bridging the gap between theory and application. It presents the most important models and methods in regression on a solid formal basis, showcasing their appropriate application through numerous examples and case studies. Key definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book's dedicated website. The availability of user-friendly software has been a key criterion for the methods selected and presented.

The chapters cover a wide range of regression models, including the classical linear model, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression, and distributional regression models. Two appendices provide detailed explanations of matrix algebra and elements of probability calculus and statistical inference.

In this extensively revised and updated new edition, the coverage of regression models has been expanded, with a focus on the relationship between regression models and machine learning. Additional details on statistical inference in structured additive regression models have been added, and a completely reworked chapter introduces distributional regression models with a comprehensive introduction. Regularization approaches are discussed in more depth in most chapters of the book.

The book is designed to cater to a diverse audience, including students, teachers, practitioners in social, economic, life sciences, statistics programs, mathematicians, and computer scientists. It provides a solid foundation in regression analysis, enabling readers to apply these techniques to real-world data and solve practical problems effectively.

Weight: 1157g
Dimension: 235 x 155 (mm)
ISBN-13: 9783662638842
Edition number: 2nd ed. 2021