Semiparametric Regression With R by Jaroslaw HarezlakSemiparametric Regression With R by Jaroslaw Harezlak

Semiparametric Regression With R

byJaroslaw Harezlak, David Ruppert, Matt P. Wand

Hardcover | December 13, 2018

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This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts. 

The authors assemble a broad range of semiparametric regression R analyses and put them in a form that is useful for applied researchers. There are chapters devoted to penalized spines, generalized additive models, grouped data, bivariate extensions of penalized spines, and spatial semi-parametric regression models. Where feasible, the R code is provided in the text, however the book is also accompanied by an external website complete with datasets and R code. Because of its flexibility, semiparametric regression has proven to be of great value with many applications in fields as diverse as astronomy, biology, medicine, economics, and finance. This book is intended for applied statistical analysts who have some familiarity with R.

David Ruppert is Professor of Statistical Science and the Andrew Schultz Jr. Professor of Engineering at the School of Operations Research and Information Engineering at Cornell University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and received the Wilcoxon Prize in 1986.  Ruppe...
Title:Semiparametric Regression With RFormat:HardcoverDimensions:331 pagesPublished:December 13, 2018Publisher:Springer NatureLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1493988514

ISBN - 13:9781493988518


Table of Contents

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