Non-parametric smoothed location model is another powerful approach which can be used
to discriminate the objects that contain both continuous and binary variables. However, the …

The location model proposed in the past is a predictive discriminant rule that can classify
new observations into one of two predefined groups based on mixtures of continuous and …

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 …

The issue of classifying objects into groups when measured variables in an experiment are
mixed has attracted the attention of statisticians. The Smoothed Location Model (SLM) …

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 …

This research's primary goal was to evaluate the performance analysis of the recently
constructed smoothed location models (SLMs) for discrimination purposes by combining two …

The mixed variable discriminant analysis procedure assumes that observations are
distributed multivariate normal with different group means but same variance-covariance …

Smoothed location model is a discriminant analysis which can be used to handle the data
involving mixtures of continuous and binary variables simultaneously. This model is …

The issue of classifying objects into groups when the measured variables are mixtures of
continuous and binary variables has attracted the attention of statisticians. Among the …

Location model is a predictive classification model that determines the groups of objects
which contain mixed categorical and continuous variables. The simplest location model is …