书目名称 | Regression | 副标题 | Models, Methods and | 编辑 | Ludwig Fahrmeir,Thomas Kneib,Brian D. Marx | 视频video | | 概述 | Provides an applied and unified introduction to parametric, nonparametric and semiparametric regression.Closes the gap between theory and application, featuring examples and applications, and user-fri | 图书封面 |  | 描述 | .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 | 出版日期 | Textbook 2021Latest edition | 关键词 | linear regression; generalized linear models; general linear mixed models; nonparametric regression; str | 版次 | 2 | doi | https://doi.org/10.1007/978-3-662-63882-8 | isbn_softcover | 978-3-662-63884-2 | isbn_ebook | 978-3-662-63882-8 | copyright | Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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