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

Yousri Slaoui ()
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Yousri Slaoui: Université de Poitiers

Sankhya A: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 13, 312-352

Abstract: 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.

Keywords: Bandwidth selection; Density estimation; Stochastic approximation algorithm; Deconvolution; Smoothing; Curve fitting; Primary 62G07; 62L20; Secondary 65D10 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13171-019-00182-3

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