The COVID-19 pandemic has created unprecedented burdens on people's health and
subjective well-being. While countries around the world have established models to track …

We study in this article some statistical methods to fit some epidemiological parameters. We
first consider a fit of the probability distribution which underlines the serial interval …

Many methods have been developed to study nonparametric function-on-function
regression models. Nevertheless, there is a lack of model selection approach to the …

In this article, we study the problem of the recursive estimator of the expectile regression of a
scalar variable Y given a random variable X that belongs in functional space. We construct a …

The Bayesian network is crucial for computer technology and artificial intelligence when
dealing with probabilities. In this paper, we extended a new semiparametric model for …

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 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 …

In this paper, we propose and investigate a new kernel regression estimators based on the
two-time-scale stochastic approximation algorithm in the case of independent functional …

In the present paper, we extend the work of Slaoui (Stat Sin 30: 417–437, 2020) in the case
of strong mixing data. Since, we are interested in nonparametric regression estimation, we …

In this paper, we propose and investigate two new kernel regression estimators based on a
bias reduction transformation technique. We study the properties of these estimators and …