<電子ブック>
Semiparametric Regression with R / by Jaroslaw Harezlak, David Ruppert, Matt P. Wand
(Use R!. ISSN:21975744)
版 | 1st ed. 2018. |
---|---|
出版者 | New York, NY : Springer New York : Imprint: Springer |
出版年 | 2018 |
大きさ | XI, 331 p. 144 illus., 142 illus. in color : online resource |
著者標目 | *Harezlak, Jaroslaw author Ruppert, David author Wand, Matt P author SpringerLink (Online service) |
件 名 | LCSH:Statistics LCSH:Biometry FREE:Statistical Theory and Methods FREE:Biostatistics FREE:Statistics in Business, Management, Economics, Finance, Insurance |
一般注記 | 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 HTTP:URL=https://doi.org/10.1007/978-1-4939-8853-2 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
|
Springer eBooks | 9781493988532 |
|
電子リソース |
|
EB00198463 |
類似資料
この資料の利用統計
このページへのアクセス回数:2回
※2017年9月4日以降