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Autore: | Harezlak Jaroslaw |
Titolo: | Semiparametric Regression with R [[electronic resource] /] / by Jaroslaw Harezlak, David Ruppert, Matt P. Wand |
Pubblicazione: | New York, NY : , : Springer New York : , : Imprint : Springer, , 2018 |
Edizione: | 1st ed. 2018. |
Descrizione fisica: | 1 online resource (341 pages) |
Disciplina: | 519.536 |
Soggetto topico: | Statistics |
R (Computer program language) | |
Statistical Theory and Methods | |
Statistics for Life Sciences, Medicine, Health Sciences | |
Statistics for Business, Management, Economics, Finance, Insurance | |
Persona (resp. second.): | RuppertDavid |
WandMatt P | |
Nota di contenuto: | Introduction -- Penalized Splines -- Generalized Additive Models -- Semiparametric Regression Analysis of Grouped Data -- Bivariate Function Extensions -- Selection of Additional Topics.-Index. |
Sommario/riassunto: | 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. |
Titolo autorizzato: | Semiparametric Regression with R |
ISBN: | 1-4939-8853-0 |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910303452503321 |
Lo trovi qui: | Univ. Federico II |
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