Testing the Optimality of Two Different Non-Parametric Discriminant Methods

Evelyn N. Okeke, Uchenna J. Okeke


This paper aims at comparing the concept of data depth to classification and classification by projection pursuit using method of linear discriminant function. These two methods allow the extension of univariate concepts to the field of multivariate analysis. In particular they open the possibility of non-parametric methods to be used in multivariate data analysis. In this study, six simulated and one real life data sets were studied and, we observed that projection pursuit method is more optimal in classifying objects into their original groups.


Variance-covariance matrix; Data depth; Spatial or L1 depth; Linear Discriminant analysis; Probability of Misclassification(PMC).

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