Publication details

A Study of Parametric and Nonparametric Kernel Density Discrimination



Year of publication 2004
Type Article in Proceedings
Conference COMPSTAT 2004, Book of Abstracts
MU Faculty or unit

Faculty of Science

Field Applied statistics, operation research
Keywords linear and quadratic discriminant analysis; nonparametric discriminant analysis; kernel density estimation; product kernels; bandwidth choice
Description This paper compares the performance of parametric and nonparametric discrimination. The multivariate product Gaussian and polynomial kernels with various data-driven choices of the bandwidth are used for density estimators and this nonparametric approaches are compared with classical one by some real and simulated data. The Matlab software environment is used for preprocessing the data and to implement proposed classification methodology. A great attention is focused to the visualization of results.
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