Semiparametric Regression with R

• Κύριοι συγγραφείς: Harezlak, Jaroslaw (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Ruppert, David (http://id.loc.gov/vocabulary/relators/aut), Wand, Matt P. (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Use R!,
• Θέματα: Statistics .; Statistical Theory and Methods.; Statistics for Life Sciences, Medicine, Health Sciences.; Statistics for Business, Management, Economics, Finance, Insurance.

 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. 

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