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Bi-level feature selection in high dimensional AFT models with applications to a genomic study.
Stat Appl Genet Mol Biol ; 18(5)2019 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-31525158
We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.





Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Idioma: Inglês Assunto da revista: Biologia Molecular / Genética Ano de publicação: 2019 Tipo de documento: Artigo País de afiliação: Estados Unidos