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

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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.

Versions

➤  Version 1 (2018-12-05)

Citation

Ryan Martin (2018). On nonparametric estimation of a mixing density via the predictive recursion algorithm. Researchers.One, https://researchers.one/articles/on-nonparametric-estimation-of-a-mixing-density-via-the-predictive-recursion-algorithm/5f52699c36a3e45f17ae7da0/v1.

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