A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating

Abstract

Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the popularly used one-shot estimation is statistically inefficient if data are not randomly distributed across different workers. To fix the problem, a novel one-step estimation method is developed with the following nice properties. First, the algorithm is communication efficient. That is the communication cost demanded is practically acceptable. Second, the resulting estimator is statistically efficient. That is its asymptotic covariance is the same as that of the global estimator. Third, the estimator is robust against data distribution. That is its consistency is guaranteed even if data are not randomly distributed across different workers. Numerical experiments are provided to corroborate our findings. A real example is also presented for illustration.

Keywords:

• Communication efficiency
• Distributed system
• One-shot estimation
• One-step estimation
• Quantile regression
• Statistical efficiency

Additional information

Funding

The research of Rui Pan is supported by National Natural Science Foundation of China (NSFC, grant nos. 11601539 and 11631003). Guodong Li’s research is supported by Hong Kong Research Grant Council (grant no. 17304617). The research of Hansheng Wang is partially supported by National Natural Science Foundation of China (NSFC, grant nos. 11831008, 11525101, and 71532001). Rui Pan and Feng Li are also supported by the Emerging Interdisciplinary Project of Central University of Finance and Economics.