Semiparametric Regression with R

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Special Collection:e-book
Format: Book
Language:English
Published: New York, NY : : Springer New York : Imprint: Springer,, 2018
Edition:1st ed. 2018.
Series:Use R!,, ISSN 2197-5736
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Online Access:https://doi.org/10.1007/978-1-4939-8853-2
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100 1 |a Harezlak, Jaroslaw.  |e szerző  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Semiparametric Regression with R  |c by Jaroslaw Harezlak, David Ruppert, Matt P. Wand. 
250 |a 1st ed. 2018. 
260 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2018 
300 |a XI, 331 p. 144 illus., 142 illus. in color.  |b online forrás 
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505 0 |a Introduction -- Penalized Splines -- Generalized Additive Models -- Semiparametric Regression Analysis of Grouped Data -- Bivariate Function Extensions -- Selection of Additional Topics.-Index. 
520 |a 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. 
580 |a Nyomtatott kiadás: ISBN 9781493988518 
580 |a Nyomtatott kiadás: ISBN 9781493988525 
506 |a Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők. 
598 |a könyv 
595 |a e-book 
650 0 |a Mathematical statistics. 
650 0 |a Statistics. 
650 1 4 |a Statistical Theory and Methods. 
650 2 4 |a Statistics for Life Sciences, Medicine, Health Sciences. 
650 2 4 |a Statistics for Business, Management, Economics, Finance, Insurance. 
653 |a elektronikus könyv 
700 1 |a Ruppert, David.  |e szerző  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Wand, Matt P.  |e szerző  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service)  |e közreadó testület 
830 0 |a Use R!,  |x 2197-5736 
856 4 0 |y Online változat  |u https://doi.org/10.1007/978-1-4939-8853-2 
850 |a B2 
264 1 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2018