Regression: Models, Methods and Applications

✍ Scribed by Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2013
• Tongue: English
• Leaves: 713
• Edition: 1
• Category: Library

✦ Synopsis

The aim of this book is an applied and unified introduction into parametric, non- 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 many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, 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 on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

✦ Table of Contents

Front Matter....Pages i-xiv
Introduction....Pages 1-19
Regression Models....Pages 21-72
The Classical Linear Model....Pages 73-175
Extensions of the Classical Linear Model....Pages 177-267
Generalized Linear Models....Pages 269-324
Categorical Regression Models....Pages 325-347
Mixed Models....Pages 349-412
Nonparametric Regression....Pages 413-533
Structured Additive Regression....Pages 535-595
Quantile Regression....Pages 597-620
Back Matter....Pages 621-698

✦ Subjects

Statistics for Business/Economics/Mathematical Finance/Insurance; Statistical Theory and Methods; Econometrics; Biostatistics; Bioinformatics; Epidemiology