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 …

Bernstein estimators are well-known to avoid the boundary bias problem of traditional kernel
estimators. The theoretical properties of these estimators have been studied extensively on …

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 complement a study recently conducted in a paper of HA Mombeni, B.
Masouri and MR Akhoond by introducing five new asymmetric kernel cdf estimators on the …

We propose and investigate a new kernel regression estimator based on the minimization of
the mean squared relative error. We study the properties of the proposed recursive estimator …

In this paper, we prove a local limit theorem for the ratio of the Poisson distribution to the
Gaussian distribution with the same mean and variance, using only elementary methods …

Recursive nonparametric density estimation for nonnegative data is considered, using an
asymmetric kernel with nonnegative support. It has a computational advantage in a situation …

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

The estimation of the distribution function of a real random variable is an intrinsic topic in
non parametric estimation. To this end, a distribution estimator based on Lagrange …

In the present paper, we are concerned with a generalized kernel estimators defined by the
stochastic approximation algorithm in the case of dependent functional data. We establish …