An Explicit Mean-Covariance Parameterization for Multivariate Response Linear Regression

Aaron J. Molstad, Guangwei Weng, Charles R. Doss, Adam J. Rothman

Research output: Contribution to journalArticlepeer-review

Abstract

We develop a new method to fit the multivariate response linear regression model that exploits a parametric link between the regression coefficient matrix and the error covariance matrix. Specifically, we assume that the correlations between entries in the multivariate error random vector are proportional to the cosines of the angles between their corresponding regression coefficient matrix columns, so as the angle between two regression coefficient matrix columns decreases, the correlation between the corresponding errors increases. We highlight two models under which this parameterization arises: a latent variable reduced-rank regression model and the errors-in-variables regression model. We propose a novel nonconvex weighted residual sum of squares criterion which exploits this parameterization and admits a new class of penalized estimators. The optimization is solved with an accelerated proximal gradient descent algorithm. Our method is used to study the association between microRNA expression and cancer drug activity measured on the NCI-60 cell lines. An R package implementing our method, MCMVR, is available online.

Original languageEnglish (US)
JournalJournal of Computational and Graphical Statistics
DOIs
StateAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
© 2021 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Covariance matrix estimation
  • Genomics
  • Measurement error
  • Multivariate regression
  • Nonconvex optimization
  • Reduced-rank regresssion

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