Testing for Marginal Linear Effects in Quantile Regression.

ABSTRACT: This paper develops a new marginal testing procedure to detect the presence of significant predictors associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then base the test on the t-statistics associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behavior due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the proposed test is applicable and computationally feasible

SUBMITTER: Wang HJ 

PROVIDER: S-EPMC5863930 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature