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Averaging of density kernel estimators

Author

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  • Chernova, O.
  • Lavancier, F.
  • Rochet, P.

Abstract

We study the theoretical properties of a linear combination of density kernel estimators obtained from different data-driven bandwidths. The average estimator is proved to be asymptotically as efficient as the oracle, with a control on the error term. The performances are tested numerically, with results that compare favorably to other existing procedures.

Suggested Citation

  • Chernova, O. & Lavancier, F. & Rochet, P., 2020. "Averaging of density kernel estimators," Statistics & Probability Letters, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:stapro:v:158:y:2020:i:c:s0167715219302913
    DOI: 10.1016/j.spl.2019.108645
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    References listed on IDEAS

    as
    1. Hall, Peter, 1982. "Limit theorems for stochastic measures of the accuracy of density estimators," Stochastic Processes and their Applications, Elsevier, vol. 13(1), pages 11-25, July.
    2. Hall, Peter & Marron, J. S., 1987. "Estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 6(2), pages 109-115, November.
    3. Lavancier, F. & Rochet, P., 2016. "A general procedure to combine estimators," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 175-192.
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