Non-asymptotic Bandwidth Selection for Density Estimation of Discrete Data

Zdravko I. Botev () and Dirk P. Kroese ()
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Zdravko I. Botev: The University of Queensland
Dirk P. Kroese: The University of Queensland

Methodology and Computing in Applied Probability, 2008, vol. 10, issue 3, 435-451

Abstract: Abstract We propose a new method for density estimation of categorical data. The method implements a non-asymptotic data-driven bandwidth selection rule and provides model sparsity not present in the standard kernel density estimation method. Numerical experiments with a well-known ten-dimensional binary medical data set illustrate the effectiveness of the proposed approach for density estimation, discriminant analysis and classification.

Keywords: Bandwidth selection; Kernel density estimator; Generalized cross entropy; Statistical modeling; Discrete data smoothing; Multivariate binary discrimination; Primary 94A17; 60K35; Secondary 68Q32; 93E14 (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/s11009-007-9057-z

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