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Canonical Correlation Analysis via Gaussian and Gamma Distributed Data

J. Opara, P. A. Esemokumo, E. U. Oti

Abstract



The study examined the canonical correlation analysis (CCA) via Gaussian and Gamma distributed data. Medical (vital signs and anthropometric indicators) data were collected for the study. Data of different sample sizes (10, 25, 40, 70, 150, 200, 250, 500, 650, 800, 1000, 1200, 1500, 2100, 2500, 2700 and 3000) were simulated from the medical data and used to perform the analysis. A computer programming language codes was written in RStudio for simulation and solution of CCA via Gaussian and Gamma data distribution. The result of the study revealed that the standard error of mean (SEM) of correlation figures for Gamma distribution is 0.04238, which was less than that of SEM of correlation figures of 0.04888 for Gaussian distribution. Further analysis using Mann-Whitney U test statistic showed that the difference in the SEM of correlation figures between the distributions was insignificant. The study thereby concluded that for CCA, the relative efficiency of the Gaussian and Gamma distributed data is the same.

Keywords


Canonical Correlation Analysis, Gaussian, Gamma, Relative Efficiency, Simulation.

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