Journal
ANALYSIS AND APPLICATIONS
Volume 18, Issue 6, Pages 1057-1075Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219530520500098
Keywords
Distributed estimator; divide and conquer; empirical processes; high-dimensional quantile regression
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Funding
- National Natural Science Foundation of China [11871287]
- Natural Science Foundation of Tianjin [18JCYBJC41100]
- Fundamental Research Funds for the Central Universities
- Research Grants Council of the Hong Kong Special Administrative Region, China [11301718, 11300519]
- NSFC [11871411]
- Shenzhen Research Institute, City University of Hong Kong
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Distributed estimation has received increasing attention in the last several years and is particularly useful in the big data setting. Both mean regression and quantile regression has been investigated recently. In this paper, we consider distributed quantile regression with high dimension using a lasso penalty for sparse modeling. We extend a previous communication-efficient approach resulting in a method for distributed quantile regression without the need to smooth the loss or the gradient of the loss. The method is simple to implement and we present some numerical studies with encouraging performances.
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