Semiparametric Regression with R

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Beschreibung

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 familiarity with R.

Über den Autor

David Ruppert is Professor of Statistical Science and the Andrew Schultz Jr. Professor of Engineering at the School of Operations Research and Information Engineering at Cornell University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and received the Wilcoxon Prize in 1986. Ruppert was named a Highly Cited researcher by ISI, now known as Clarivate Analytics. His current research focuses on astrostatistics, measurement error models, splines, semiparametric regression, and environmental statistics. He has published over 100 articles in refereed journals and has published five books.He is a past editor of the Journal of the American Statistical Association and of the Electronic Journal of Statistics

Jaroslaw Harezlak is Associate Professor in the Department of Epidemiology and Biostatistics at the Indiana University School of Public Health in Bloomington. He has a Ph.D. in biostatistics from the Harvard University. After a 2-year post-doctoral training at the Harvard School of Public Health, he joined Indiana University as an Assistant Professor. His interests span a number of medical areas: including NeuroHIV, physical activity, sexually transmitted infections, and concussions, as well as statistical areas: including semiparametric regression, functional data analysis and structured high-dimensional data. In his applied research, he uses data arising in structural and functional brain imaging, accelerometry, and intensively collected longitudinal studies. He has published over 50 peer-reviewed articles in the statistical, medical and epidemiological journals as well as 3 invited chapters.

Matt Wand is Distinguished Professor of Statistics at University of Technology Sydney. He serves as an associate editor for the Statistics journal: Australian and New Zealand Journal of Statistics. Professor Wand is chiefly interested in the development of statistical methodology for finding useful structure in large multivariate data sets. Currently, Wand's specific interests include expectation propagation, message passing algorithms, variational approximate methods, statistical methods for streaming data, generalized linear mixed models, and semiparametric regression.

Zusammenfassung

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 methodology and their use in practice

Because of its flexibility, semiparametric regression has proven to be of great value in many applications in fields as diverse as astronomy, biology, medicine, economics and finance

Inhaltsverzeichnis

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

Details

Erscheinungsjahr:	2018
Fachbereich:	Wahrscheinlichkeitstheorie
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Reihe:	Use R!
Inhalt:	xi 331 S. 2 s/w Illustr. 142 farbige Illustr. 331 p. 144 illus. 142 illus. in color.
ISBN-13:	9781493988518
ISBN-10:	1493988514
Sprache:	Englisch
Herstellernummer:	978-1-4939-8851-8
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Autor:	Harezlak, Jaroslaw Wand, Matt P. Ruppert, David
Auflage:	1st ed. 2018
Hersteller:	Springer New York Springer US, New York, N.Y. Use R!
Maße:	235 x 155 x 19 mm
Von/Mit:	Jaroslaw Harezlak (u. a.)
Erscheinungsdatum:	13.12.2018
Gewicht:	0,522 kg

Artikel-ID: 114093483