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Quantile trace regression via nuclear norm regularization

Author

Listed:
  • Wang, Lei
  • Zhang, Jing
  • Li, Bo
  • Liu, Xiaohui

Abstract

Trace regression models are widely used to accommodate matrix-type covariates, such as panel data, images, genomics microarrays, etc. In this paper, we extend the trace regression to the quantile trace regression model. The optimal convergence rate of the estimator is derived under mild conditions. Some simulations are carried out for illustration. Finally, we apply the proposed method to a students’ behavior data set related to personalized education.

Suggested Citation

  • Wang, Lei & Zhang, Jing & Li, Bo & Liu, Xiaohui, 2022. "Quantile trace regression via nuclear norm regularization," Statistics & Probability Letters, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:stapro:v:182:y:2022:i:c:s0167715221002613
    DOI: 10.1016/j.spl.2021.109299
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    References listed on IDEAS

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