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Determining optimal rank in reduced rank regression model by the likelihood ratio test | ||
International Journal of Nonlinear Analysis and Applications | ||
مقاله 254، دوره 13، شماره 2، مهر 2022، صفحه 3173-3181 اصل مقاله (384.51 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.28133.3812 | ||
نویسنده | ||
Saad Abed Madhi* | ||
Department of Medical Instrumentation Technologies Engineering, Hilla University College, Babylon, Iraq | ||
تاریخ دریافت: 28 دی 1400، تاریخ بازنگری: 14 اسفند 1400، تاریخ پذیرش: 22 فروردین 1401 | ||
چکیده | ||
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. | ||
کلیدواژهها | ||
Multivariate Reduced Rank Regression؛ Singular Value Decomposition؛ More-Penrose inverse؛ Maximum likelihood estimation | ||
مراجع | ||
[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. | ||
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