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Hellinger distance and Kullback--Leibler loss for the kernel density estimator

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  • Kanazawa, Yuichiro

Abstract

The optimal window width, which asymptotically minimizes mean Hellinger distance between the kernel estimator and density, is known to be equivalent to the one that maximizes expected Kullback--Leibler loss for compactly supported densities. Implications of the result are discussed.

Suggested Citation

  • Kanazawa, Yuichiro, 1993. "Hellinger distance and Kullback--Leibler loss for the kernel density estimator," Statistics & Probability Letters, Elsevier, vol. 18(4), pages 315-321, November.
    • Handle: RePEc:eee:stapro:v:18:y:1993:i:4:p:315-321
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    Cited by:

    1. Trimborn, Simon & Härdle, Wolfgang Karl, 2018. "CRIX an Index for cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 107-122.
    2. Jones, M. C., 1995. "On two recent papers of Y. Kanazawa," Statistics & Probability Letters, Elsevier, vol. 24(3), pages 269-271, August.

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