Recursive density estimators based on Robbins-Monro's scheme and using Bernstein polynomials

04/14/2019
by   Yousri SLAOUI, et al.
0

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 polynomials to construct a recursive density estimator. We study the asymptotic properties of the proposed recursive estimator. We then compared our proposed recursive estimator with many others estimators. Finally, we confirm our theoretical result through a simulation study and then using two real datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/25/2018

Bivariate density estimation using normal-gamma kernel with application to astronomy

We consider the problem of estimation of a bivariate density function wi...
research
05/06/2020

Dependence structure estimation using Copula Recursive Trees

We construct the Copula Recursive Tree (CORT) estimator: a flexible, con...
research
07/23/2023

Efficient Exact Quadrature of Regular Solid Harmonics Times Polynomials Over Simplices in ℝ^3

A generalization of a recently introduced recursive numerical method for...
research
05/31/2018

Central limit theorems for the L_p-error of smooth isotonic estimators

We investigate the asymptotic behavior of the L_p-distance between a mon...
research
03/19/2019

Relative Efficiency of Higher Normed Estimators Over the Least Squares Estimator

In this article, we study the performance of the estimator that minimize...
research
11/28/2018

Simple Local Polynomial Density Estimators

This paper introduces an intuitive and easy-to-implement nonparametric d...
research
07/17/2018

Model selection for sequential designs in discrete finite systems using Bernstein kernels

We view sequential design as a model selection problem to determine whic...

Please sign up or login with your details

Forgot password? Click here to reset