International Journal of Nonlinear Analysis and Applications

Determining optimal rank in reduced rank regression model by the likelihood ratio test

Document Type : Research Paper

Author

Department of Medical Instrumentation Technologies Engineering, Hilla University College, Babylon, Iraq

Abstract

This paper presents a method for determining the actual rank of the coefficients matrix in the reduced rank multivariate regression model. The method is constructed using the singular value decomposition and the Likelihood Ratio Test(LRT). Some illustrative examples are given to verify this method.

Keywords

[1] Willin Alvarez and V. J. Griffin, Estimation procedure for reduced rank regression, passed, Statistics, Optimization and Information Computing 4 (2016), no. 2, 107–117.
[2] P. T. Davies and M. K-S. Tso, Procedures for reduced-rank regression, Applied Statistics 31 (1982), 244.
[3] Irving John Good, Some applications of the singular decomposition of a matrix, Technometrics 11 (1969), 823–831.
[4] Paul E Green, Mathematical tools for applied multivariate analysis, Academic Press, 2014.
[5] Alan Julian Izenman, Reduced-rank regression for the multivariate linear model, Journal of multivariate analysis 5 (1975), 248–264.
[6] Saad Abed Madhi, Reduced-rank regression model estimation and rank determination, University of Manchester, 1981.
[7] Saad Abed Madhi and Samira Faisal Abushilah, Selecting optimal rank in reduced rank regression by aic, IJAAR 5 (2021), 43–51.
International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 2
July 2022
Pages 3173-3181
  • Receive Date: 18 January 2022
  • Revise Date: 05 March 2022
  • Accept Date: 11 April 2022