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Semiparametric Regression with R

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Semiparametric Regression with R/ by Jaroslaw Harezlak, David Ruppert, Matt P. Wand.
作者:	Harezlak, Jaroslaw.
其他作者:	Ruppert, David.
面頁冊數:	XI, 331 p. 144 illus., 142 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics . -
電子資源:	https://doi.org/10.1007/978-1-4939-8853-2
ISBN:	9781493988532

Semiparametric Regression with R
Harezlak, Jaroslaw.

Semiparametric Regression with R[electronic resource] /by Jaroslaw Harezlak, David Ruppert, Matt P. Wand. - 1st ed. 2018. - XI, 331 p. 144 illus., 142 illus. in color.online resource. - Use R!,2197-5736. - Use R!,.

Introduction -- Penalized Splines -- Generalized Additive Models -- Semiparametric Regression Analysis of Grouped Data -- Bivariate Function Extensions -- Selection of Additional Topics.-Index.

This easy-to-follow applied book expands upon the authors’ prior work on semiparametric regression to include the use of R software. In 2003, authors Ruppert and Wand co-wrote Semiparametric Regression with R.J. Carroll, which introduced the techniques and benefits of semiparametric regression in a concise and user-friendly fashion. Fifteen years later, semiparametric regression is applied widely, powerful new methodology is continually being developed, and advances in the R computing environment make it easier than ever before to carry out analyses. Semiparametric Regression with R introduces the basic concepts of semiparametric regression with a focus on applications and R software. This volume features case studies from environmental, economic, financial, and other fields. The examples and corresponding code can be used or adapted to apply semiparametric regression to a wide range of problems. It contains more than fifty exercises, and the accompanying HRW package contains all datasets and scripts used in the book, as well as some useful R functions. This book is suitable as a textbook for advanced undergraduates and graduate students, as well as a guide for statistically-oriented practitioners, and could be used in conjunction with Semiparametric Regression. Readers are assumed to have a basic knowledge of R and some exposure to linear models. For the underpinning principles, calculus-based probability, statistics, and linear algebra are desirable.

ISBN: 9781493988532

Standard No.: 10.1007/978-1-4939-8853-2doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Semiparametric Regression with R
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