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Variable selection methods in high-dimensional regression: a simulation study

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Resumo:A challenging problem in the analysis of high-dimensional data is variable selection. In this study, we describe a bootstrap based technique for selecting predictors in partial least-squares regression (PLSR) and principle component regression (PCR) in high-dimensional data. Using a bootstrap-based technique for significance tests of the regression coefficients, a subset of the original variables can be selected to be included in the regression, thus obtaining a more parsimonious model with smaller prediction errors. We compare the bootstrap approach with several variable selection approaches (jack-knife and sparse formulation-based methods) on PCR and PLSR in simulation and real data.
Autores principais:Shahriari, Shirin
Outros Autores:Faria, Susana; Gonçalves, A. Manuela
Assunto:High-dimensional data Partial least-squares regression Principle component regression Variable selection Bootstrap
Ano:2015
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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