Location Model is a classification approach that capable to deal with mixed binary and continuous variables at once. The binary variables create segmentation in the groups called cells whilst the continuous variables measure the differences between groups based on information inside
the cells. It is important to note that location model is biased and even impossible to be constructed when involving some empty cells. Interestingly from previous studies, smoothing approach managed to remedy the effects of some empty cells. However, numerical analysis has demonstrated that
the performances of the location model based on smoothing approach are good in most situations except if there are outliers in the sample. Thus, the presence of outliers has alarmed us to further investigating the performance of the location model. Therefore, in this paper, we develop a new
methodology of location model producing new model called automatic trimmed location model through new estimators resulting from an integration of automatic trimming and smoothing approaches in addressing both issues of outliers and empty cells simultaneously. The results have confirmed that
the new methodology developed as well as the new location model produced offer another potential tools to practitioners, which possible to be considered in classification problems when the data samples contain outliers and at the same time could resolve the crisis of some empty cells of the
location model.
Strategies for Non-Parametric Smoothing of the Location Model in Mixed-Variable Discriminant Analysis
