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A new non parametric estimator for Pdf based on inverse gamma distribution

Author

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  • A. M. Mousa
  • M. Kh. Hassan
  • A. Fathi

Abstract

The non parametric approach is considered to estimate probability density function (Pdf) which is supported on(0, ∞). This approach is the inverse gamma kernel. We show that it has same properties as gamma, reciprocal inverse Gaussian, and inverse Gaussian kernels such that it is free of the boundary bias, non negative, and it achieves the optimal rate of convergence for the mean integrated squared error. Also some properties of the estimator were established such as bias and variance. Comparison of the bandwidth selection methods for inverse gamma kernel estimation of Pdf is done.

Suggested Citation

  • A. M. Mousa & M. Kh. Hassan & A. Fathi, 2016. "A new non parametric estimator for Pdf based on inverse gamma distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(23), pages 7002-7010, December.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:23:p:7002-7010
    DOI: 10.1080/03610926.2014.972575
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    Cited by:

    1. Ouimet, Frédéric & Tolosana-Delgado, Raimon, 2022. "Asymptotic properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 187(C).

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