Trace regression model with simultaneously low rank and row(column) sparse parameter

Junlong Zhao, Lu Niu and Shushi Zhan

Computational Statistics & Data Analysis, 2017, vol. 116, issue C, 1-18

Abstract: In this paper, we consider the trace regression model with matrix covariates, where the parameter is a matrix of simultaneously low rank and row(column) sparse. To estimate the parameter, we formulate a convex optimization problem with the nuclear norm and group Lasso penalties, and propose an alternating direction method of multipliers (ADMM) algorithm. The asymptotic properties of the estimator are established. Simulation results confirm the effectiveness of our method.

Keywords: Trace regression model; Matrix covariates; Low rank; Row/column sparse; ADMM algorithm (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:116:y:2017:i:c:p:1-18

DOI: 10.1016/j.csda.2017.06.009

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