GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA.

ABSTRACT: Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L₁ penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quanti

SUBMITTER: Zheng Q 

PROVIDER: S-EPMC4654965 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature