Semiparametric Regression with R / by Jaroslaw Harezlak, David Ruppert, Matt P. Wand

Von:

Harezlak, Jaroslaw [VerfasserIn]

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Use R! | Springer eBook Collection | SpringerLink BücherVerlag: New York, NY : Springer New York, 2018Beschreibung: Online-Ressource (XI, 331 p. 144 illus., 142 illus. in color, online resource)ISBN:

9781493988532

Schlagwörter:

Andere physische Formen: 9781493988518 | 9781493988525 | Erscheint auch als: 978-1-4939-8851-8 Druck-Ausgabe | Printed edition: 9781493988518 | Printed edition: 9781493988525 DDC-Klassifikation:

519.5

MSC: MSC: *62-01 | 62G08 | 62-04LOC-Klassifikation:

QA276-280

DOI: DOI: 10.1007/978-1-4939-8853-2Online-Ressourcen:

Zusammenfassung: 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 desirableZusammenfassung: Introduction -- Penalized Splines -- Generalized Additive Models -- Semiparametric Regression Analysis of Grouped Data -- Bivariate Function Extensions -- Selection of Additional Topics.-IndexPPN: PPN: 1045542806Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS

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