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Novel and simple non-parametric methods of estimating the joint and marginal densities

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  • Alghalith, Moawia

Abstract

We introduce very simple non-parametric methods that overcome key limitations of the existing literature on both the joint and marginal density estimation. In doing so, we do not assume any form of the marginal distribution or joint distribution a priori. Furthermore, our method circumvents the bandwidth selection problems. We compare our method to the kernel density method.

Suggested Citation

  • Alghalith, Moawia, 2016. "Novel and simple non-parametric methods of estimating the joint and marginal densities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 94-98.
    • Handle: RePEc:eee:phsmap:v:454:y:2016:i:c:p:94-98
    DOI: 10.1016/j.physa.2016.02.034
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    References listed on IDEAS

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    1. David E. Allen & Michael McAleer & Abhay K. Singh, 2017. "Risk Measurement and Risk Modelling Using Applications of Vine Copulas," Sustainability, MDPI, vol. 9(10), pages 1-34, September.
    2. Alghalith, Moawia, 2008. "Recent applications of theory of the firm under uncertainty," European Journal of Operational Research, Elsevier, vol. 186(2), pages 443-450, April.
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    10. Weining Shen & Surya T. Tokdar & Subhashis Ghosal, 2013. "Adaptive Bayesian multivariate density estimation with Dirichlet mixtures," Biometrika, Biometrika Trust, vol. 100(3), pages 623-640.
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    Cited by:

    1. Alghalith, Moawia, 2017. "A new parametric method of estimating the joint probability density," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 799-803.
    2. Moawia Alghalith, 2022. "Methods in Econophysics: Estimating the Probability Density and Volatility," Papers 2301.10178, arXiv.org.

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