In this article we describe a new Hermite series based sequential estimator for the
Spearman rank correlation coefficient and provide algorithms applicable in both the …

In this paper we propose an automatic selection of the bandwidth of the semi-recursive
kernel estimators of a regression function defined by the stochastic approximation algorithm …

In this paper, we consider the alleviation of the boundary problem when the probability
density function has bounded support. We apply Robbins-Monro's algorithm and Bernstein …

We propose a recursive distribution estimator using Robbins-Monro's algorithm and
Bernstein polynomials. We study the properties of the recursive estimator, as a competitor of …

In this paper, we propose an automatic selection of the bandwidth of the recursive kernel
estimators of a regression function defined by the stochastic approximation algorithm. We …

In this article, we investigate various asymptotic properties (bias, variance, mean squared
error, mean integrated squared error, asymptotic normality, uniform strong consistency) for …

In this paper, we consider estimating the parameters of univariate α-stable distributions and
their mixtures. First, using a Gaussian kernel density distribution estimator, we propose an …

In this paper, we extend the work of Slaoui (J Probab Stat, https://doi. org/10.1155/2014/
739640, 2014) to the case of strong mixing data. Then, we study the properties of these …

In the present paper we propose recursive general kernel-type estimators for spatial data
defined by the stochastic approximation algorithm. We obtain the central limit theorem and …

In the present paper, we are mainly concerned with the kernel type estimators for the
moment generating function. More precisely, we establish the central limit theorem together …