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On the Nonconsistency of Maximum Likelihood Nonparametric Density Estimators

In: Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface

Author

Listed:
  • Eugene F. Schuster

    (The University of Texas at El Paso)

  • Gavin G. Gregory

    (The University of Texas at El Paso)

Abstract

One criterion proposed in the literature for selecting the smoothing parameter(s) in RosenblattParzen nonparametric constant kernel estimators of a probability density function is a leave-out-one-at-a-time nonparametric maximum likelihood method. Empirical work with this estimator in the univariate case showed that it worked quite well for short tailed distributions. However, it drastically oversmoothed for long tailed distributions. In this paper it is shown that this nonparametric maximum likelihood method will not select consistent estimates of the density for long tailed distributions such as the double exponential and Cauchy distributions. A remedy which was found for estimating long tailed distributions was to apply the nonparametric maximum likelihood procedure to a variable kernel class of estimators. This paper considers one data set, which is a pseudo-random sample of size 100 from a Cauchy distribution, to illustrate the problem with the leave-out-one-at-a-time nonparametric maximum likelihood method and to illustrate a remedy to this problem via a variable kernel class of estimators.

Suggested Citation

  • Eugene F. Schuster & Gavin G. Gregory, 1981. "On the Nonconsistency of Maximum Likelihood Nonparametric Density Estimators," Springer Books, in: William F. Eddy (ed.), Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, pages 295-298, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-9464-8_43
    DOI: 10.1007/978-1-4613-9464-8_43
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