# On nonparametric estimation of a mixing density via the predictive recursion algorithm

arXiv: https://arxiv.org/abs/1812.02149 · HTML: https://mlsanity.com/paper/1812.02149/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Quasi-Bayes meets Vines](https://mlsanity.com/paper/2406.12764/) (2024)
- [Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates](https://mlsanity.com/paper/2206.06462/) (2022)
- [Multiple Hypothesis Testing Framework for Spatial Signals](https://mlsanity.com/paper/2108.12314/) (2021)

## Abstract (arXiv)

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.