The stochastic approximation method for estimation of a distribution function
Y Slaoui - Mathematical Methods of Statistics, 2014 - Springer
We apply the stochastic approximation method to construct a large class of recursive kernel
estimators of a distribution function. We study the properties of these estimators and …
estimators of a distribution function. We study the properties of these estimators and …
Bandwidth selection for recursive kernel density estimators defined by stochastic approximation method
Y Slaoui - Journal of Probability and Statistics, 2014 - Wiley Online Library
We propose an automatic selection of the bandwidth of the recursive kernel estimators of a
probability density function defined by the stochastic approximation algorithm introduced by …
probability density function defined by the stochastic approximation algorithm introduced by …
Optimal bandwidth selection for semi-recursive kernel regression estimators
Y Slaoui - arXiv preprint arXiv:1607.00963, 2016 - arxiv.org
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 …
kernel estimators of a regression function defined by the stochastic approximation algorithm …
Recursive density estimators based on Robbins-Monro's scheme and using Bernstein polynomials
Y Slaoui, A Jmaei - arXiv preprint arXiv:1904.06675, 2019 - arxiv.org
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 …
density function has bounded support. We apply Robbins-Monro's algorithm and Bernstein …
Recursive kernel estimator in a semiparametric regression model
EDD Nkou - Journal of Nonparametric Statistics, 2023 - Taylor & Francis
Sliced inverse regression (SIR) is a recommended method to identify and estimate the
central dimension reduction (CDR) subspace. CDR subspace is at the base to describe the …
central dimension reduction (CDR) subspace. CDR subspace is at the base to describe the …
Recursive distribution estimator defined by stochastic approximation method using Bernstein polynomials
A Jmaei, Y Slaoui, W Dellagi - Journal of Nonparametric Statistics, 2017 - Taylor & Francis
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 …
Bernstein polynomials. We study the properties of the recursive estimator, as a competitor of …
Revisiting R\'ev\'esz's stochastic approximation method for the estimation of a regression function
A Mokkadem, M Pelletier, Y Slaoui - arXiv preprint arXiv:0812.3973, 2008 - arxiv.org
In a pioneer work, R\'ev\'esz (1973) introduces the stochastic approximation method to build
up a recursive kernel estimator of the regression function $ x\mapsto E (Y| X= x) $. However …
up a recursive kernel estimator of the regression function $ x\mapsto E (Y| X= x) $. However …
Plug‐in bandwidth selector for recursive kernel regression estimators defined by stochastic approximation method
Y Slaoui - Statistica Neerlandica, 2015 - Wiley Online Library
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 …
estimators of a regression function defined by the stochastic approximation algorithm. We …
Recursive nonparametric regression estimation for independent functional data
Y Slaoui - Statistica Sinica, 2020 - JSTOR
We propose an automatic selection of the bandwidth of the recursive nonparametric
estimation of the regression function defined by the stochastic approximation algorithm …
estimation of the regression function defined by the stochastic approximation algorithm …
Large and moderate deviation principles for recursive kernel density estimators defined by stochastic approximation method
Y Slaoui - arXiv preprint arXiv:1301.6392, 2013 - arxiv.org
In this paper we prove large and moderate deviations principles for the recursive kernel
estimators of a probability density function defined by the stochastic approximation algorithm …
estimators of a probability density function defined by the stochastic approximation algorithm …