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

版	2nd ed. 2021.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XX, 746 p. 286 illus., 4 illus. in color : online resource
著者標目	*Fahrmeir, Ludwig author Kneib, Thomas author Lang, Stefan author Marx, Brian D author SpringerLink (Online service)
件　名	LCSH:Regression analysis LCSH:Statistics  LCSH:Quantitative research LCSH:Nonparametric statistics LCSH:Mathematical statistics FREE:Linear Models and Regression FREE:Applied Statistics FREE:Data Analysis and Big Data FREE:Non-parametric Inference FREE:Parametric Inference FREE:Statistical Theory and Methods
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 -- Distributional Regression Models 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 HTTP:URL=https://doi.org/10.1007/978-3-662-63882-8

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