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Data-driven Deconvolution Recursive Kernel Density Estimators Defined by Stochastic Approximation Method

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  • Yousri Slaoui

    (Université de Poitiers)

Abstract

In this paper we show how one can implement in practice the bandwidth selection in deconvolution recursive kernel estimators of a probability density function defined by the stochastic approximation algorithm. We consider the so called super smooth case where the characteristic function of the known distribution decreases exponentially. We show that, using the proposed bandwidth selection and some special stepsizes, the proposed recursive estimator will be very competitive to the nonrecursive one in terms of estimation error and much better in terms of computational costs. We corroborate these theoretical results through simulations and a real dataset.

Suggested Citation

  • Yousri Slaoui, 2021. "Data-driven Deconvolution Recursive Kernel Density Estimators Defined by Stochastic Approximation Method," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 312-352, February.
    • Handle: RePEc:spr:sankha:v:83:y:2021:i:1:d:10.1007_s13171-019-00182-3
    DOI: 10.1007/s13171-019-00182-3
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    References listed on IDEAS

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