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Bayesian adaptive bandwidth selector for multivariate discrete kernel estimator

Author

Listed:
  • Nawal Belaid
  • Smail Adjabi
  • Célestin C. Kokonendji
  • Nabil Zougab

Abstract

We treat a non parametric estimator for joint probability mass function, based on multivariate discrete associated kernels which are appropriated for multivariate count data of small and moderate sample sizes. Bayesian adaptive estimation of the vector of bandwidths using the quadratic and entropy loss functions is considered. Exact formulas for the posterior distribution and the vector of bandwidths are obtained. Numerical studies indicate that the performance of our approach is better, comparing with other bandwidth selection techniques using integrated squared error as criterion. Some applications are made on real data sets.

Suggested Citation

  • Nawal Belaid & Smail Adjabi & Célestin C. Kokonendji & Nabil Zougab, 2018. "Bayesian adaptive bandwidth selector for multivariate discrete kernel estimator," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(12), pages 2988-3001, June.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:12:p:2988-3001
    DOI: 10.1080/03610926.2017.1346807
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    Cited by:

    1. Xijian Hu & Yaori Lu & Huiguo Zhang & Haijun Jiang & Qingdong Shi, 2021. "Selection of the Bandwidth Matrix in Spatial Varying Coefficient Models to Detect Anisotropic Regression Relationships," Mathematics, MDPI, vol. 9(18), pages 1-14, September.
    2. Célestin C. Kokonendji & Sobom M. Somé, 2021. "Bayesian Bandwidths in Semiparametric Modelling for Nonnegative Orthant Data with Diagnostics," Stats, MDPI, vol. 4(1), pages 1-22, March.
    3. Michael Govorov & Giedrė Beconytė & Gennady Gienko, 2023. "Trivariate Kernel Density Estimation of Spatiotemporal Crime Events with Case Study for Lithuania," Sustainability, MDPI, vol. 15(11), pages 1-17, May.

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