We propose a penalized quantile regression and an independence screening procedure to identify important covariates and to exclude unimportant ones for a general class of ultrahigh dimensional single ...

This study focuses on using a high-dimensional error-in-variables regression to identify a small number of important interpretable factors from corrupted data in applications in which measurement ...