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＜電子ブック＞
Regression : Models, Methods and Applications / by Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx

版	1st ed. 2013.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2013
本文言語	英語
大きさ	XIV, 698 p : online resource
著者標目	*Fahrmeir, Ludwig author Kneib, Thomas author Lang, Stefan author Marx, Brian author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Econometrics LCSH:Biometry LCSH:Epidemiology FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Statistical Theory and Methods FREE:Econometrics FREE:Biostatistics FREE:Statistics FREE:Epidemiology
一般注記	Introduction -- Regression Models -- The Classical Linear Model -- Extensions of the Classical Linear Model -- Generalized Linear Models -- Categorical Regression Models -- Mixed Models -- Nonparametric Regression -- Structured Additive Regression -- Quantile Regression -- A Matrix Algebra -- B Probability Calculus and Statistical Inference -- Bibliography -- Index 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 HTTP:URL=https://doi.org/10.1007/978-3-642-34333-9

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