Sparse reduced-rank regression for multivariate varying-coefficient models. Issue 4 (4th March 2021)

• Record Type: Journal Article
• Authors: Zhang, Fode; Li, Rui; Lian, Heng; Bandyopadhyay, Dipankar
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 4(2021)
• Page Start: 752
• Page End: 767
• Publication Date: 2021-03-04
• Subjects: Rank regression -- variable selection -- varying-coefficient models; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1829622
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: 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.