We consider the recursive estimation of the probability density function of continuous
random variables from a strongly mixing random sample. We revisit here earlier research on …

In this article, our central focus is to investigate the non parametric estimator of the
probability density or mass function of a data stream by using the general family of kernels …

High-frequency data is widely used and studied in many fields. In this paper, the asymptotic
normality of kernel density estimator under ρ-mixing high-frequency data is studied. We first …

This article mainly studies the asymptotic properties of recursive kernel density estimation for
high-frequency data with a long time span. Under appropriate conditions, the variance, bias …

We construct an optimal exponential tail decreasing confidence region for an unknown
density of distribution in the Lebesgue-Riesz as well as in the uniform} norm, built on the …

In this work, we extend to the case of the strong mixing data the results of Khardani and
Semmar. A kernel-type recursive estimator of the conditional density function is introduced …

The considerations of this paper are restricted to random variables with values on
Riemannian manifolds M and hence we propose a geometric framework to estimate their …

The paper deals with the estimation of the parameters of stochastic processes that are
discretely observed. We construct estimator of the parameters based on the minimum …

In this paper, we propose a recursive estimators of the regression function based on the two-
time-scale stochastic approximation algorithms and the Bernstein polynomials. We study the …

In this paper, we propose a relative recursive regression estimator for censored data defined
by the stochastic approximation algorithm to deal with the presence of outliers or when the …