Selecting the derivative of a functional covariate in scalar-on-function regression
This paper presents tests to formally choose between regression models using different derivatives of a functional covariate in scalar-on-function regression. We demonstrate that for linear regression, models using different derivatives can be nested within a model that includes point-impact effects...
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| Veröffentlicht in: | Statistics and computing 2022-06, Vol.32 (3), Article 35 |
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| creator | Hooker, Giles Shang, Han Lin |
| description | This paper presents tests to formally choose between regression models using different derivatives of a functional covariate in scalar-on-function regression. We demonstrate that for linear regression, models using different derivatives can be nested within a model that includes point-impact effects at the end-points of the observed functions. Contrasts can then be employed to test the specification of different derivatives. When nonlinear regression models are employed, we apply a
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test to determine the statistical significance of the nonlinear structure between a functional covariate and a scalar response. The finite-sample performance of these methods is verified in simulation, and their practical application is demonstrated using both chemometric and environmental data sets. |
| doi_str_mv | 10.1007/s11222-022-10091-5 |
| format | Article |
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C
test to determine the statistical significance of the nonlinear structure between a functional covariate and a scalar response. The finite-sample performance of these methods is verified in simulation, and their practical application is demonstrated using both chemometric and environmental data sets.</description><identifier>ISSN: 0960-3174</identifier><identifier>EISSN: 1573-1375</identifier><identifier>DOI: 10.1007/s11222-022-10091-5</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial Intelligence ; Computer Science ; Derivatives ; Probability and Statistics in Computer Science ; Regression models ; Statistical analysis ; Statistical Theory and Methods ; Statistics and Computing/Statistics Programs</subject><ispartof>Statistics and computing, 2022-06, Vol.32 (3), Article 35</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2345-9342d1446743fa28e768c459a0376d9a0e98238a1814eeed87df25be9dea3f953</citedby><cites>FETCH-LOGICAL-c2345-9342d1446743fa28e768c459a0376d9a0e98238a1814eeed87df25be9dea3f953</cites><orcidid>0000-0003-1769-6430 ; 0000-0003-2648-1167</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11222-022-10091-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11222-022-10091-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Hooker, Giles</creatorcontrib><creatorcontrib>Shang, Han Lin</creatorcontrib><title>Selecting the derivative of a functional covariate in scalar-on-function regression</title><title>Statistics and computing</title><addtitle>Stat Comput</addtitle><description>This paper presents tests to formally choose between regression models using different derivatives of a functional covariate in scalar-on-function regression. We demonstrate that for linear regression, models using different derivatives can be nested within a model that includes point-impact effects at the end-points of the observed functions. Contrasts can then be employed to test the specification of different derivatives. When nonlinear regression models are employed, we apply a
C
test to determine the statistical significance of the nonlinear structure between a functional covariate and a scalar response. The finite-sample performance of these methods is verified in simulation, and their practical application is demonstrated using both chemometric and environmental data sets.</description><subject>Artificial Intelligence</subject><subject>Computer Science</subject><subject>Derivatives</subject><subject>Probability and Statistics in Computer Science</subject><subject>Regression models</subject><subject>Statistical analysis</subject><subject>Statistical Theory and Methods</subject><subject>Statistics and Computing/Statistics Programs</subject><issn>0960-3174</issn><issn>1573-1375</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKt_wFPAczTJ5GNzlOIXCB6q5xB3J3XLuluTbcF_b-oq3jwM7wzzzsvwEHIu-KXg3F5lIaSUjJcqsxNMH5CZ0BaYAKsPyYw7wxkIq47JSc5rzoUwoGZkucQO67HtV3R8Q9pgandhbHdIh0gDjdu-LIc-dLQediG1YUTa9jTXoQuJDT37ddCEq4Q5l_aUHMXQZTz70Tl5ub15Xtyzx6e7h8X1I6slKM0cKNkIpYxVEIOs0JqqVtoFDtY0RdBVEqogKqEQsalsE6V-RddggOg0zMnFlLtJw8cW8-jXwzaVX7OXRoM2EpwqLjm56jTknDD6TWrfQ_r0gvs9PD_B8wWe_4bn99EwHeVi7leY_qL_ufoCcSRxwQ</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Hooker, Giles</creator><creator>Shang, Han Lin</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-1769-6430</orcidid><orcidid>https://orcid.org/0000-0003-2648-1167</orcidid></search><sort><creationdate>20220601</creationdate><title>Selecting the derivative of a functional covariate in scalar-on-function regression</title><author>Hooker, Giles ; Shang, Han Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2345-9342d1446743fa28e768c459a0376d9a0e98238a1814eeed87df25be9dea3f953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial Intelligence</topic><topic>Computer Science</topic><topic>Derivatives</topic><topic>Probability and Statistics in Computer Science</topic><topic>Regression models</topic><topic>Statistical analysis</topic><topic>Statistical Theory and Methods</topic><topic>Statistics and Computing/Statistics Programs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hooker, Giles</creatorcontrib><creatorcontrib>Shang, Han Lin</creatorcontrib><collection>CrossRef</collection><jtitle>Statistics and computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hooker, Giles</au><au>Shang, Han Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Selecting the derivative of a functional covariate in scalar-on-function regression</atitle><jtitle>Statistics and computing</jtitle><stitle>Stat Comput</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>32</volume><issue>3</issue><artnum>35</artnum><issn>0960-3174</issn><eissn>1573-1375</eissn><abstract>This paper presents tests to formally choose between regression models using different derivatives of a functional covariate in scalar-on-function regression. We demonstrate that for linear regression, models using different derivatives can be nested within a model that includes point-impact effects at the end-points of the observed functions. Contrasts can then be employed to test the specification of different derivatives. When nonlinear regression models are employed, we apply a
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| subjects | Artificial Intelligence Computer Science Derivatives Probability and Statistics in Computer Science Regression models Statistical analysis Statistical Theory and Methods Statistics and Computing/Statistics Programs |
| title | Selecting the derivative of a functional covariate in scalar-on-function regression |
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