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Regressionmodels, methods and applic...

Fahrmeir, L.

Regressionmodels, methods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Regressionby Ludwig Fahrmeir ... [et al.].
其他題名:	models, methods and applications /
其他作者:	Fahrmeir, L.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg :2021.
面頁冊數:	1 online resource (xx, 744 p.) :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Regression analysis.
電子資源:	https://doi.org/10.1007/978-3-662-63882-8
ISBN:	9783662638828$q(electronic bk.)

Regressionmodels, methods and applications /
Regressionmodels, methods and applications /[electronic resource] :by Ludwig Fahrmeir ... [et al.]. - Second edition. - Berlin, Heidelberg :Springer Berlin Heidelberg :2021. - 1 online resource (xx, 744 p.) :ill., digital ;24 cm.

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.

ISBN: 9783662638828$q(electronic bk.)

Standard No.: 10.1007/978-3-662-63882-8doiSubjects--Topical Terms:

181872
Regression analysis.

LC Class. No.: QA278.2 / F34 2021

Dewey Class. No.: 519.536

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