On nonparametric estimation of a mixing density via the predictive recursion algorithm
Methodology
2022-09-15 v1
Abstract
Nonparametric estimation of a mixing density based on observations from the corresponding mixture is a challenging statistical problem. This paper surveys the literature on a fast, recursive estimator based on the predictive recursion algorithm. After introducing the algorithm and giving a few examples, I summarize the available asymptotic convergence theory, describe an important semiparametric extension, and highlight two interesting applications. I conclude with a discussion of several recent developments in this area and some open problems.
Cite
@article{arxiv.1812.02149,
title = {On nonparametric estimation of a mixing density via the predictive recursion algorithm},
author = {Ryan Martin},
journal= {arXiv preprint arXiv:1812.02149},
year = {2022}
}
Comments
22 pages, 5 figures. Comments welcome at https://www.researchers.one/article/2018-12-5