International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2018, Vol. 3, Issue 2, Part E

Variable selection for identification of fatty liver cases: Nonparametric discriminant analysis


Author(s): S Padmanaban and Martin L William

Abstract: A forward-model-building algorithm for discriminating two populations in a nonparametric setting has been recently introduced by Padmanaban and William (2016) wherein the restrictions existing in the traditional discriminant analysis have been removed. This latest approach is applicable without assumptions on the two underlying populations. As an application of this approach, we consider the discrimination and classification of fatty liver cases from others using some observable variables that are believed to be related to the health condition of the liver. The discriminant model is developed with a sample of 160 cases drawn from a case-control study. We shall also compare the discriminant model performance with that of logistic regression.

Pages: 321-325 | Views: 443 | Downloads: 25

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How to cite this article:
S Padmanaban, Martin L William. Variable selection for identification of fatty liver cases: Nonparametric discriminant analysis. Int J Stat Appl Math 2018;3(2):321-325.
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