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Type: Artigo
Title: Consistent Variable Selection For Functional Regression Models
Author: Collazos
Julian A. A.; Dias
Ronaldo; Zambom
Adriano Z.
Abstract: The 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.
Subject: B-splines
Hypotheses Testing
False Discovery Rate
Functional Data
Likelihood Ratio Test
Editor: Elsevier INC
San Diego
Rights: fechado
Identifier DOI: 10.1016/j.jmva.2015.06.007
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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