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Nonparametric estimation of possibly similar densities

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  • Ker, Alan P.

Abstract

A class of nonparametric methods is developed to estimate a set of possibly similar densities that offers greater efficiency if they are similar while seemingly not losing any if they are not. Theoretical properties and finite sample performance are promising.

Suggested Citation

  • Ker, Alan P., 2016. "Nonparametric estimation of possibly similar densities," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 23-30.
    • Handle: RePEc:eee:stapro:v:117:y:2016:i:c:p:23-30
    DOI: 10.1016/j.spl.2016.03.010
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    Citations

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    Cited by:

    1. Zongyuan Shang & Alan Ker, 2021. "Two generalized nonparametric methods for estimating like densities," Computational Statistics, Springer, vol. 36(1), pages 113-126, March.
    2. Yong Liu & Alan P. Ker, 2021. "Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 231-257, March.
    3. Alan P. Ker & Yong Liu, 2017. "Bayesian model averaging of possibly similar nonparametric densities," Computational Statistics, Springer, vol. 32(1), pages 349-365, March.

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