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Titlebook: Regression; Models, Methods and Ludwig Fahrmeir,Thomas Kneib,Brian D. Marx Textbook 2021Latest edition Springer-Verlag GmbH Germany, part

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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Titlebook: Regression; Models, Methods and  Ludwig Fahrmeir,Thomas Kneib,Brian D. Marx Textbook 2021Latest edition Springer-Verlag GmbH Germany, part
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
doihttps://doi.org/10.1007/978-3-662-63882-8
isbn_softcover978-3-662-63884-2
isbn_ebook978-3-662-63882-8
copyrightSpringer-Verlag GmbH Germany, part of Springer Nature 2021
The information of publication is updating

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Categorical Regression Models, may express their opinion about some product in ordered categories ranging from “very satisfied” to “not satisfied at all.” Similarly, voters choose between several parties or they assess the quality of candidates in ordered categories.
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Introduction, grown children. He examined the . between the body heights of the children and the average body height of both parents. To adjust for the natural height differences across gender, the body height of women was multiplied by a factor of 1.08. In order to better examine this relationship, he listed all his data in a contingency table (Table .).
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Introduction,ally known for the Galton board which provides a demonstration of the principles underlying the binomial distribution. At the end of the nineteenth century, Galton was mainly interested in questions regarding heredity. Galton collected extensive data illustrating the body height of parents and their
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The Classical Linear Model,ect. .. In addition to their direct application, linear regression models are also the basis of a variety of more complex regression methods. Examples are generalized linear models (Chap. .), mixed models (Chap. .), or semiparametric models (Chaps. . and .).
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Generalized Linear Models,propriate transformation is needed to ensure approximate normality of the response. In addition, the expectation of the response is assumed to be a linear combination of covariates. Again, these covariates may be transformed before being included in the linear predictor. However, in many application
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Categorical Regression Models,y applications response variables often have more than two categories. For example, consumers may choose between different brands of a product or they may express their opinion about some product in ordered categories ranging from “very satisfied” to “not satisfied at all.” Similarly, voters choose
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