Robustness of the fisher's discriminant function to skew-curved normal distribution

Maja Sever (Author), Jaro Lajovic (Author), Borut Rajer (Author)

Abstract

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 data.

Keywords

Diskriminantna analiza;Multivariatna analiza;

Data

Language:	English
Year of publishing:	2005
Typology:	1.01 - Original Scientific Article
Organization:	UL FDV - Faculty of Social Sciences
Publisher:	Fakulteta za družbene vede
UDC:	303
COBISS:	24315741
ISSN:	1854-0023
Views:	921
Downloads:	196
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Unknown
Secondary keywords:	Discriminant analysis;Multivariate analysis;
URN:	URN:NBN:SI
Type (COBISS):	Not categorized
Pages:	str. 231-242
Volume:	ǂVol. ǂ2
Issue:	ǂno. ǂ2
Chronology:	2005
Keywords (UDC):	social sciences;družbene vede;methods of the social sciences;metode družbenih ved;
ID:	1467970