23 KB113 KB18 KB200420242005Lajovic, JaroRajer, BorutSever, Maja12 straništevilka:2letnik:2str. 231-242ISSN:1854-0023ISSN:1854-0031COBISSID:24315741URN:URN:NBN:SI:doc-OBKJOOKWenFakulteta za družbene vedeMetodološki zvezkiDiscriminant analysisDiskriminantna analizamultivariantna analizaMultivariate analysisMultivariatna analizastatistične metodeRobustness of the Fisher's discriminant function to skew-curved normal distribution|Discriminant analysis is a widely used multivariate technique with Fisher's discriminant analysis (FDA) being its most venerable form. FDA assumes equality of population covariance matrices, but does not require multivariate normality. Nevertheless, the latter is desirable for optimal classification. To test FDA's performance under non-normality caused by skewness the method was assessed with simulation based on a skew-curved normal (SCN) distribution belonging to the family of skew-generalised normal distributions; additionally, effects of sample size and rotation were evaluated. Apparent error rate (APER) was used as the measure of classification performance. The analysis was performed using ANOVA with (transformed) mean APER as the dependent variable. Results show the FDA to be highly robust to skewness introduced into the model via the SCN distributed simulated dataTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za družbene vede