@book{
Solr-opac-EUL01-000979157,
title = {Semiparametric Regression with R},
series = {Use R!,, ISSN 2197-5736},
author = {Harezlak, Jaroslaw., szerző},
editor = {Ruppert, David., szerző},
editor = {Wand, Matt P., szerző},
publisher = {New York, NY : : Springer New York : Imprint: Springer},
year = {2018},
edition = {1st ed. 2018.},
pages = {331},
note = {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.},
url = {https://doi.org/10.1007/978-1-4939-8853-2}
}