 Adaptive distributed smooth composite quantile regression estimation for large-scale dataKangning Wang, Jingyu Zhang and Xiaofei Sun Computational Statistics & Data Analysis, 2025, vol. 204, issue C Abstract: Composite quantile regression (CQR) is a good statistical learning tool because of its estimation efficiency and robustness advantages, but the growing size of modern data is bringing challenges to it. First, the non-smoothness of CQR loss function poses high computation burden in large-scale problems. Second, although some distributed CQR algorithms have been proposed, they heavily rely on uniformity and randomness conditions, which are frequently violated in practice. To address these issues, this article first proposes a smooth CQR by constructing a smooth loss, which can converge to the original non-smooth loss uniformly. Then a distributed CQR is developed, in which the estimator can be calculated conveniently by minimizing a pilot sample-based distributed surrogate loss. In particular, it can be adaptive when the uniformity or randomness condition is violated. The established theoretical results and numerical experiments all confirm the proposed methods. Keywords: Composite quantile regression; Big data; Smooth loss; Distributed algorithm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:204:y:2025:i:c:s0167947324001944 DOI: 10.1016/j.csda.2024.108110 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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