Sparse reduced-rank regression for multivariate varying-coefficient models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Fode Zhang
  • Rui Li
  • Heng Lian
  • Dipankar Bandyopadhyay

Related Research Unit(s)


Original languageEnglish
Pages (from-to)752–767
Number of pages17
Journal / PublicationJournal of Statistical Computation and Simulation
Issue number4
Online published15 Oct 2020
Publication statusPublished - 2021


Varying-coefficient regression is a popular statistical tool that models the way a certain variable modulates the effect of other predictors nonlinearly. However, a majority of the VC regression models consider univariate responses; the case of multivariate responses have received relatively lesser attention. In this paper, we propose a robust multivariate varying-coefficient model based on rank loss that models the relationships among different responses via reduced-rank regression and penalized variable selection. Some asymptotic results are also established for the proposed methods. Using synthetic data, we investigate the finite sample performance and robustness properties of the estimator. We also illustrate our methodology by application to a real dataset on periodontal disease.

Research Area(s)

  • Rank regression, variable selection, varying-coefficient models