Regression: Models, Methods and Applications

Description

Applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application Written in textbook style suitable for students, the material is close to current research on advanced regression analysis Availability of (user-friendly) software is a major criterion for the methods selected and presented Many examples and applications from diverse fields illustrate models and methods Most of the data sets are available via http://www.regressionbook.org/ 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. Content Level » Graduate Keywords » generalized linear models - linear regression - mixed models - semiparametric regression - spatial regression Related subjects » Business, Economics & Finance - Econometrics / Statistics - Public Health - Statistical Theory and Methods - Systems Biology and Bioinformatics