Consistent Variable Selection For Functional Regression Models

dc.creatorCollazos
dc.creatorJulian A. A.; Dias
dc.creatorRonaldo; Zambom
dc.creatorAdriano Z.
dc.date2016
dc.dateabr
dc.date2017-11-13T11:34:44Z
dc.date2017-11-13T11:34:44Z
dc.date.accessioned2018-03-29T05:48:56Z
dc.date.available2018-03-29T05:48:56Z
dc.identifierJournal Of Multivariate Analysis. Elsevier Inc, v. 146, p. 63 - 71, 2016.
dc.identifier0047-259X
dc.identifierWOS:000373648200006
dc.identifier10.1016/j.jmva.2015.06.007
dc.identifierhttp://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S0047259X15001529
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/326484
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1363490
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionThe dual problem of testing the predictive significance of a particular covariate, and identification of the set of relevant covariates is common in applied research and methodological investigations. To study this problem in the context of functional linear regression models with predictor variables observed over a grid and a scalar response, we consider basis expansions of the functional covariates and apply the likelihood ratio test. Based on p-values from testing each predictor, we propose a new variable selection method, which is consistent in selecting the relevant predictors from set of available predictors that is allowed to grow with the sample size n. Numerical simulations suggest that the proposed variable selection procedure outperforms existing methods found in the literature. A real dataset from weather stations in Japan is analyzed. (C) 2016 Published by Elsevier Inc.
dc.description146
dc.description63
dc.description71
dc.descriptionCNPq [302956/2013-1]
dc.descriptionFapesp [2013/07375-0, 2013/00506-1]
dc.descriptionCAPES
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.languageEnglish
dc.publisherElsevier INC
dc.publisherSan Diego
dc.relationJournal of Multivariate Analysis
dc.rightsfechado
dc.sourceWOS
dc.subjectB-splines
dc.subjectHypotheses Testing
dc.subjectFalse Discovery Rate
dc.subjectFunctional Data
dc.subjectLikelihood Ratio Test
dc.titleConsistent Variable Selection For Functional Regression Models
dc.typeArtículos de revistas


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