Online estimation of integrated squared density derivatives

Abdelkader Mokkadem and Mariane Pelletier

Statistics & Probability Letters, 2020, vol. 166, issue C

Abstract: Hall and Marron (1987) introduced kernel estimators of integrals of the squared m-order derivatives of a probability density. Mokkadem and Pelletier (2020) gave recursive versions of their estimators, but the main drawback of these estimators is that their update requires the use of all past data. The aim of this paper is the study of online versions of the estimators introduced by Hall and Marron (1987), that is of estimators which are not only recursive, but which also have the property that their update uses only the last available data. Rates of convergence in mean squared error (MSE) are calculated. Similarly to the estimators of Hall and Marron (1987) and of Mokkadem and Pelletier (2020), our online estimators achieve the parametric rate n−1 when m=0 or when higher order kernels are used. For the case when the parametric rate is not obtained, we also study an online version of the estimator proposed by Jones and Sheather (1991). Finally, we provide recursive estimators of the optimal bandwidth in the framework of density estimation.

Keywords: Online estimation; Recursive estimation; Kernel estimators of integrals; Stochastic algorithm; Mean squared error; Rate of convergence (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.spl.2020.108880

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