Ontology highlight
ABSTRACT:
SUBMITTER: Ma S
PROVIDER: S-EPMC5603281 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Ma Shujie S Li Runze R Tsai Chih-Ling CL
Journal of the American Statistical Association 20170330 518
In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution ...[more]