The parameter estimation of the multivariate matrix regression models

  • Zerong Lin School of Mathematics and Physics Suzhou University of Science and Technology,Suzhou, China
  • Lingling He School of Mathematics and Physics Suzhou University of Science and Technology,Suzhou, China
  • Tian Wu School of Mathematics and Physics Suzhou University of Science and Technology,Suzhou, China
  • Changqing Xu School of Mathematics and Physics Suzhou University of Science and Technology,Suzhou, China
DOI: https://doi.org/10.19139/soic.v6i2.361
Keywords: Multivariate matrix model, high order derivative, likelihood estimation, vectorization, parameter matrix

Abstract

In this paper, we consider the parameter matrix estimation problem of the multivariate matrix regression models. We approximate the parameter matrix $B$ and the covariance matrix by using the method of the maximum likelihood estimation, together with the Kronecker product of matrices, vectorization of matrices and matrix derivatives.

Author Biography

Changqing Xu, School of Mathematics and Physics Suzhou University of Science and Technology,Suzhou, China
Professor of MathematicsResearch area: Statistical and multivariate and multilinear data analysis    

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Published
2018-06-24
How to Cite
Lin, Z., He, L., Wu, T., & Xu, C. (2018). The parameter estimation of the multivariate matrix regression models. Statistics, Optimization & Information Computing, 6(2), 286-291. https://doi.org/10.19139/soic.v6i2.361
Issue
Vol 6 No 2 (2018)
Section
Research Articles
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