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A Study on Performance of Linear Discriminant Function under Multivariate Non-Normal Situations

KrishiKosh

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Title A Study on Performance of Linear Discriminant Function under Multivariate Non-Normal Situations
M Sc
 
Creator ROHAN KUMAR RAMAN
 
Contributor A. K. Paul
 
Subject biological phenomena, sampling, maize, rice, byproducts, marketing, statistical methods, sets, objects, genotypes
 
Description T-8236
Discriminant analysis deals with the problem of classification. Generally multiple of measurements are available on an individual and on the basis of these measurements one can classify a new variable into one of the several well defined categories. The performance of linear discriminant function is studied under multivariate non-normal situations. The different multivariate non-normal populations are simulated by using the mean vectors and dispersion matrix of rice and maize data sets. Further fifty different independent samples each are simulated for different dimensions and sample sizes for maize and rice data to obtain empirical probabilities of misclassification. On fitting linear discriminant function to non-normal data the empirical probabilities of misclassification are higher as compared to using normal approximation. In large sample sizes and in higher dimensions the differences between empirical and normal approximation of probabilities of misclassification are negligible and almost equal.
 
Date 2016-09-28T19:39:56Z
2016-09-28T19:39:56Z
2010
 
Type Thesis
 
Identifier http://krishikosh.egranth.ac.in/handle/1/79397
 
Format application/pdf
 
Publisher IARI, INDIAN AGRICULTURAL STATISTICS RESEARCH INSTITUTE