Semiparametric Regression with R

Description

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. Front Matter ....Pages i-xi Introduction (Jaroslaw Harezlak, David Ruppert, Matt P. Wand)....Pages 1-14 Penalized Splines (Jaroslaw Harezlak, David Ruppert, Matt P. Wand)....Pages 15-70 Generalized Additive Models (Jaroslaw Harezlak, David Ruppert, Matt P. Wand)....Pages 71-128 Semiparametric Regression Analysis of Grouped Data (Jaroslaw Harezlak, David Ruppert, Matt P. Wand)....Pages 129-172 Bivariate Function Extensions (Jaroslaw Harezlak, David Ruppert, Matt P. Wand)....Pages 173-220 Selection of Additional Topics (Jaroslaw Harezlak, David Ruppert, Matt P. Wand)....Pages 221-314 Back Matter ....Pages 315-331