| 书目名称 | Semiparametric Regression with R | | 编辑 | Jaroslaw Harezlak,David Ruppert,Matt P. Wand | | 视频video | http://file.papertrans.cn/866/865010/865010.mp4 | | 概述 | It is accompanied by the web-site: semiparametric-regression-with-r.net and contains pointers to datasets and R code relevant to this book.This book helps to close the gap between the available method | | 丛书名称 | Use R! | | 图书封面 |  | | 描述 | 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 fa | | 出版日期 | Book 2018 | | 关键词 | semiparametric regression analysis; regression analysis; bivariate function extensions; generalized add | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4939-8853-2 | | isbn_softcover | 978-1-4939-8851-8 | | isbn_ebook | 978-1-4939-8853-2Series ISSN 2197-5736 Series E-ISSN 2197-5744 | | issn_series | 2197-5736 | | copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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