Optimal subsampling algorithm for composite quantile regression with distributed data

Xiaohui Yuan (), Shiting Zhou () and Yue Wang ()
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Xiaohui Yuan: Changchun University of Technology
Shiting Zhou: Changchun University of Technology
Yue Wang: Changchun University of Technology

Computational Statistics, 2025, vol. 40, issue 9, No 2, 4936 pages

Abstract: Abstract For massive data stored on multiple machines, we propose a distributed subsampling procedure for the composite quantile regression. By establishing the consistency and asymptotic normality of the composite quantile regression estimator from a general subsampling algorithm, we derive the optimal subsampling probabilities and the optimal allocation sizes under the L-optimality criteria. A two-step algorithm is developed to approximate the optimal subsampling procedure. The proposed methods are illustrated through numerical experiments on simulated and real datasets.

Keywords: Composite quantile regression; Distributed data; Massive data; Optimal subsampling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01570-6

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