Non-asymptotic Bandwidth Selection for Density Estimation of Discrete Data
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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.
KeywordsBandwidth selection Kernel density estimator Generalized cross entropy Statistical modeling Discrete data smoothing Multivariate binary discrimination
AMS 2000 Subject ClassificationPrimary 94A17 60K35 Secondary 68Q32 93E14
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