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Robust Composite Quantile Regression with Large‐scale Streaming Data Sets

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  • Kangning Wang
  • Di Zhang
  • Xiaofei Sun

Abstract

Composite quantile regression (CQR) has advantages in robustness and high estimation efficiency. In modern statistical learning, we often encounter streaming data sets with unbounded cumulative data sizes. However, limited computer memory and non‐smoothness of CQR objective function pose challenges to methods and algorithms. An interesting issue is how to implement CQR in the streaming data setting. To address this issue, this article first constructs a smooth CQR, and then an online renewable CQR procedure is proposed. In theory, the oracle property of the proposed renewable estimator is established, which gives theoretical guarantees. Numerical experiments also confirm the proposed methods.

Suggested Citation

  • Kangning Wang & Di Zhang & Xiaofei Sun, 2025. "Robust Composite Quantile Regression with Large‐scale Streaming Data Sets," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 52(2), pages 736-755, June.
    • Handle: RePEc:bla:scjsta:v:52:y:2025:i:2:p:736-755
    DOI: 10.1111/sjos.12769
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