期刊名称:International Journal of Economics and Finance
印刷版ISSN:1916-971X
电子版ISSN:1916-9728
出版年度:2019
卷号:11
期号:5
页码:103-113
DOI:10.5539/ijef.v11n5p103
出版社:Canadian Center of Science and Education
摘要:The estimation of the error rates is of vital importance in classification problems as this is used as a basis to choose the best discriminant function; that is, the one with a minimum misclassification error. The quadratic discriminant function (QDF), Euclidean Distance Classifier (EDC), and Fisher’s Linear Discriminant Function (FLDC) have been in use for a long time for the purpose of classification. In this paper, we compare the misclassification error rate of the QDF, EDC, and FLDC with the Vine Copulas based on Gaussian and Clayton models. The results were obtained for the general case where the means are unequal and the covariance matrices are unequal.