Penalized Quantile Regression for Distributed Big Data Using the Slack Variable Representation
Ye Fan, Nan Lin, Xianjun Yin
Abstract
Penalized quantile regression is a widely used tool for analyzing high-dimensional data with heterogeneity. Although its estimation theory has been well studied in the literature, its computation still remains a challenge in big data, due to the nonsmoothness of the check loss function and the possible nonconvexity of the penalty term. In this article, we propose the QPADM-slack method, a parallel algorithm formulated via the alternating direction method of multipliers (ADMM) that supports penalized quantile regression in big data. Our proposal is different from the recent QPADM algorithm and uses the slack variable representation of the quantile regression problem. Simulation studies demonstrate that this new formulation is significantly faster than QPADM, especially when the data volume n or the dimension p is large, and has favorable estimation accuracy in big data analysis for both nondistributed and distributed environments. We further illustrate the practical performance of QPADM-slack by analyzing a news popularity dataset.