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Non-asymptotic Bandwidth Selection for Density Estimation of Discrete Data

Author

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  • Zdravko I. Botev

    (The University of Queensland)

  • Dirk P. Kroese

    (The University of Queensland)

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.

Suggested Citation

  • Zdravko I. Botev & Dirk P. Kroese, 2008. "Non-asymptotic Bandwidth Selection for Density Estimation of Discrete Data," Methodology and Computing in Applied Probability, Springer, vol. 10(3), pages 435-451, September.
    • Handle: RePEc:spr:metcap:v:10:y:2008:i:3:d:10.1007_s11009-007-9057-z
    DOI: 10.1007/s11009-007-9057-z
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    References listed on IDEAS

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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    2. R. Y. Rubinstein, 2005. "A Stochastic Minimum Cross-Entropy Method for Combinatorial Optimization and Rare-event Estimation," Methodology and Computing in Applied Probability, Springer, vol. 7(1), pages 5-50, March.
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    Cited by:

    1. Zdravko I. Botev & Dirk P. Kroese, 2011. "The Generalized Cross Entropy Method, with Applications to Probability Density Estimation," Methodology and Computing in Applied Probability, Springer, vol. 13(1), pages 1-27, March.

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