摘要：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 compare our proposed recursive estimator with many others estimators. Finally, we confirm our theoretical result through a simulation study and then using two real datasets..
