The performance of the linear and quadratic discriminant functions for three types of non-normal distribution

The purpose of thls paper is to investlgate the performance of the LDF (linear discrlmlnant functlon) and QDF (quadratic dlscrminant functlon) for classlfylng observations from the three types of univariate and multivariate non-normal dlstrlbutlons on the basls of the mlsclasslficatlon rate. The theoretical and the empirical results are described for unlvariate distributions, and the empirical results are presented for multivariate distributions. It 1s also shown that the sign of the skewness of each population and the kurtosis have essential effects on the performance of the two discriminant functions. The variations of the populatlon speclflc mlsclasslflcatlon rates are greatly depend on the sample slze. For the large dlmenslonal populatlon dlstributlons, if the sample sizes are sufflclent, the QDF performs better than the LDF. We show the crlterla of a cholce between the two discriminant functions as an application.