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Winsorized and Smoothed Estimation of the Location Model in Mixed Variables Discrimination


Hamid, Hashibah (2018) Winsorized and Smoothed Estimation of the Location Model in Mixed Variables Discrimination. Applied Mathematics & Information Sciences, 12 (1). pp. 133-138. ISSN 1935-0090

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Abstract

View references (45)The location model is a familiar basis and excellent tool for discriminant analysis of mixtures of categorical and continuous variables compared to other existing discrimination methods.However, the presence of outliers affects the estimation of population parameters, hence causing the inability of the location model to provide accurate statistical model and interpretation as well.In this paper, we construct a new location model through the integration of Winsorization and smoothing approach taking into account mixed variables in the presence of outliers.The newly constructed model successfully enhanced the model performance compared to the earlier developed location models. The results of analysis proved that this new location model can be used as an alternative method for discrimination tasks as for academicians and practitioners in future applications, especially when they encountered outliers problem and had some empty cells in the data sample.

Item Type: Article
Uncontrolled Keywords: Classification Discriminant analysis Error rate Mixed variables Smoothing approach Winsorization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Quantitative Sciences
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 23 Jul 2018 01:10
Last Modified: 23 Jul 2018 01:10
URI: http://repo.uum.edu.my/id/eprint/24428

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