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Semiparametric regression with R

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Semiparametric regression with R/ by Jaroslaw Harezlak, David Ruppert, Matt P. Wand.
Author:	Harezlak, Jaroslaw.
other author:	Ruppert, David.
Published:	New York, NY :Springer New York : : 2018.,
Description:	xi, 331 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Regression analysis. -
Online resource:	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. - New York, NY :Springer New York :2018. - xi, 331 p. :ill. (some col.), digital ;24 cm. - 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:

569541
Regression analysis.

LC Class. No.: QA278.2 / .H374 2018

Dewey Class. No.: 519.536

Semiparametric regression with R
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