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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id opac-EUL01-000979157
collection e-book
institution L_200
EUL01
spelling Harezlak, Jaroslaw. szerző aut http://id.loc.gov/vocabulary/relators/aut
Semiparametric Regression with R by Jaroslaw Harezlak, David Ruppert, Matt P. Wand.
1st ed. 2018.
New York, NY : Springer New York : Imprint: Springer, 2018
XI, 331 p. 144 illus., 142 illus. in color. online forrás
szöveg txt rdacontent
számítógépes c rdamedia
távoli hozzáférés cr rdacarrier
szövegfájl PDF rda
Use R!, 2197-5736
Introduction -- Penalized Splines -- Generalized Additive Models -- Semiparametric Regression Analysis of Grouped Data -- Bivariate Function Extensions -- Selection of Additional Topics.-Index.
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.
Nyomtatott kiadás: ISBN 9781493988518
Nyomtatott kiadás: ISBN 9781493988525
Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők.
könyv
e-book
Mathematical statistics.
Statistics.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.
elektronikus könyv
Ruppert, David. szerző aut http://id.loc.gov/vocabulary/relators/aut
Wand, Matt P. szerző aut http://id.loc.gov/vocabulary/relators/aut
SpringerLink (Online service) közreadó testület
Online változat https://doi.org/10.1007/978-1-4939-8853-2
EUL01
language English
format Book
author Harezlak, Jaroslaw., szerző
spellingShingle Harezlak, Jaroslaw., szerző
Semiparametric Regression with R
Use R!,, ISSN 2197-5736
Mathematical statistics.
Statistics.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.
elektronikus könyv
author_facet Harezlak, Jaroslaw., szerző
Ruppert, David., szerző
Wand, Matt P., szerző
SpringerLink (Online service)
author2 Ruppert, David., szerző
Wand, Matt P., szerző
author_corporate SpringerLink (Online service)
author_sort Harezlak, Jaroslaw.
title Semiparametric Regression with R
title_short Semiparametric Regression with R
title_full Semiparametric Regression with R by Jaroslaw Harezlak, David Ruppert, Matt P. Wand.
title_fullStr Semiparametric Regression with R by Jaroslaw Harezlak, David Ruppert, Matt P. Wand.
title_full_unstemmed Semiparametric Regression with R by Jaroslaw Harezlak, David Ruppert, Matt P. Wand.
title_auth Semiparametric Regression with R
title_sort semiparametric regression with r
series Use R!,, ISSN 2197-5736
series2 Use R!,
publishDate 2018
publishDateSort 2018
physical XI, 331 p. 144 illus., 142 illus. in color. : online forrás
edition 1st ed. 2018.
isbn 978-1-4939-8853-2
issn 2197-5736
callnumber-first Q - Science
callnumber-subject QA - Mathematics
callnumber-label QA276-280
callnumber-raw 979157
callnumber-search 979157
topic Mathematical statistics.
Statistics.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.
elektronikus könyv
topic_facet Mathematical statistics.
Statistics.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.
elektronikus könyv
Mathematical statistics.
Statistics.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.
url https://doi.org/10.1007/978-1-4939-8853-2
illustrated Not Illustrated
dewey-hundreds 500 - Science
dewey-tens 510 - Mathematics
dewey-ones 519 - Probabilities & applied mathematics
dewey-full 519.5
dewey-sort 3519.5
dewey-raw 519.5
dewey-search 519.5
first_indexed 2021-04-02T14:47:03Z
last_indexed 2021-04-04T08:17:53Z
recordtype opac
publisher New York, NY : : Springer New York : Imprint: Springer,
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score 13,333304
generalnotes 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.