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Kernel Density Estimation with Generalized Binning

Author

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  • M. Pawlak
  • U. Stadtmuller

Abstract

We propose kernel density estimators based on prebinned data. We use generalized binning schemes based on the quantiles points of a certain auxiliary distribution function. Therein the uniform distribution corresponds to usual binning. The statistical accuracy of the resulting kernel estimators is studied, i.e. we derive mean squared error results for the closeness of these estimators to both the true function and the kernel estimator based on the original data set. Our results show the influence of the choice of the auxiliary density on the binned kernel estimators and they reveal that non‐uniform binning can be worthwhile.

Suggested Citation

  • M. Pawlak & U. Stadtmuller, 1999. "Kernel Density Estimation with Generalized Binning," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(4), pages 539-561, December.
    • Handle: RePEc:bla:scjsta:v:26:y:1999:i:4:p:539-561
    DOI: 10.1111/1467-9469.00167
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    Cited by:

    1. Adriano Z. Zambom & Ronaldo Dias, 2013. "A Review of Kernel Density Estimation with Applications to Econometrics," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 20-42, April.

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