title: Asymptotic performance of the quadratic discriminant function to skewed training samples.
abstract: This study investigates the asymptotic performance of the quadratic discriminant function (QDF) under skewed training samples. The main objective of this study is to evaluate the performance of the QDF under skewed distribution considering different sample size ratios, varying the group centroid separators and the number of variables. Three populations [Formula: see text] with increasing group centroid separator function were considered. A multivariate normal distributed data was simulated with MatLab R2009a. There was an increase in the average error rates of the sample size ratios 1:2:2 and 1:2:3 as the total sample size increased asymptotically in the skewed distribution when the centroid separator increased from 1 to 3. The QDF under the skewed distribution performed better for the sample size ratio 1:1:1 as compared to the other sampling ratios and under centroid separator [Formula: see text].
authors.affiliation: Department of Mathematics, Kwame Nkrumah University of Science and Technology, PMB KNUST, Kumasi, Ghana.
authors.forename: Atinuke
authors.initials: A
authors.lastname: Adebanji
authors.affiliation: Institute of Research, Innovation and Development (IRID), Kumasi Polytechnic, Box 854, Kumasi, Ghana.
authors.forename: Michael
authors.initials: M
authors.lastname: Asamoah-Boaheng
authors.affiliation: Department of Mathematics, Kwame Nkrumah University of Science and Technology, PMB KNUST, 24105 Kumasi, Ghana.
authors.forename: Olivia
authors.initials: O
authors.lastname: Osei-Tutu
keywords: Coefficient of Variation; Error rates; Group centroid separator; Lognormal distribution