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

Authors

Hashibah Hamid, School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, MalaysiaFollow

Author Country (or Countries)

Malaysia

Abstract

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.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/120112

Recommended Citation

Hamid, Hashibah (2018) "Winsorized and Smoothed Estimation of the Location Model in Mixed Variables Discrimination," Applied Mathematics & Information Sciences: Vol. 12: Iss. 1, Article 12.
DOI: http://dx.doi.org/10.18576/amis/120112
Available at: https://digitalcommons.aaru.edu.jo/amis/vol12/iss1/12