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

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

Povzetek

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.

Ključne besede

Diskriminantna analiza;Multivariatna analiza;

Podatki

Jezik:	Angleški jezik
Leto izida:	2005
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FDV - Fakulteta za družbene vede
Založnik:	Fakulteta za družbene vede
UDK:	303
COBISS:	24315741
ISSN:	1854-0023
Št. ogledov:	921
Št. prenosov:	196
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Neznan jezik
Sekundarne ključne besede:	Discriminant analysis;Multivariate analysis;
URN:	URN:NBN:SI
Vrsta dela (COBISS):	Delo ni kategorizirano
Strani:	str. 231-242
Letnik:	ǂVol. ǂ2
Zvezek:	ǂno. ǂ2
Čas izdaje:	2005
Ključne besede (UDK):	social sciences;družbene vede;methods of the social sciences;metode družbenih ved;
ID:	1467970