Structured variable selection with q-values.
Biostatistics
; 14(4): 695-707, 2013 Sep.
Article
em En
| MEDLINE
| ID: mdl-23580317
ABSTRACT
When some of the regressors can act on both the response and other explanatory variables, the already challenging problem of selecting variables when the number of covariates exceeds the sample size becomes more difficult. A motivating example is a metabolic study in mice that has diet groups and gut microbial percentages that may affect changes in multiple phenotypes related to body weight regulation. The data have more variables than observations and diet is known to act directly on the phenotypes as well as on some or potentially all of the microbial percentages. Interest lies in determining which gut microflora influence the phenotypes while accounting for the direct relationship between diet and the other variables A new methodology for variable selection in this context is presented that links the concept of q-values from multiple hypothesis testing to the recently developed weighted Lasso.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Interpretação Estatística de Dados
/
Modelos Estatísticos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Biostatistics
Ano de publicação:
2013
Tipo de documento:
Article
País de afiliação:
Estados Unidos