Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Stat Appl Genet Mol Biol ; 18(5)2019 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-31525158

RESUMO

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.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA