Comparison of Error of Classification Associated With Gamma Distribution Using Some Selected Discriminant Functions

Abstract

This study examined the error of classification associated with the gamma distribution for the linear discriminant analysis, the logistic discriminant analysis and the quadratic discriminant analysis. Data were simulated data from Gamma distribution for sample size 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100. Fifty simulation for each of the sample size was performed for the methods considered in this study. The findings of the study revealed that for the gamma distribution, the logistic discriminant analysis method performed better followed by the linear discriminant analysis and then the quadratic discriminant analysis. This is because the logistic discriminant analysis method has the least average misclassification error rate across the sample sizes followed by the linear discriminant analysis method while the quadratic discriminant method performed least for gamma distribution.