Volume : X, Issue : V, May - 2020

Abstract :

This paper provides a nonparametric discriminant stepwise algorithm to discriminate two multivariate populations and an optimal decision rule for classification of a member to either of the two populations. This ‘forward–stepwise‘ recently proposed for the classification problem by Padmanaban and William (2016a,). As has been done in the above–referred papers, this paper relaxes the ‘equal variance–covariance matrices‘ condition traditionally imposed and develops a discrimination–classification procedure by including variables that contribute to effective discrimination in a ‘forward’ manner one–by–one. The inclusion of variables in the discriminant is determined on the basis of maximum discriminating ability and exclusion is on the basis of least ‘discriminating ability‘ as reflected in ‘difference‘ between the distributions of the discriminant in the two populations. A decision–rule for classification or membership–prediction with a view to maximizing correct predictions is provided as done in the forward and backward approaches referred above. The proposed algorithm is applied to develop an optimal discriminant for predicting Low back pain among Teachers in the city of Chennai, India, and its performance is compared with logistic regression

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Article: Download PDF    DOI : 10.36106/ijar  

Cite This Article:

, INDIAN JOURNAL OF APPLIED RESEARCH : Volume-10 | Issue-5 | May-2020


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