Fast nonparametric near-maximum likelihood estimation of a mixing density

Minwoo Chae; Ryan Martin; Stephen G. Walker

doi:10.1016/j.spl.2018.05.012

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arXiv · 1712.03852

Fast nonparametric near-maximum likelihood estimation of a mixing density

Minwoo Chae·Ryan Martin·Stephen G. Walker

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Abstract

Mixture models are regularly used in density estimation applications, but the problem of estimating the mixing distribution remains a challenge. Nonparametric maximum likelihood produce estimates of the mixing distribution that are discrete, and these may be hard to interpret when the true mixing distribution is believed to have a smooth density. In this paper, we investigate an algorithm that produces a sequence of smooth estimates that has been conjectured to converge to the nonparametric maximum likelihood estimator. Here we give a rigorous proof of this conjecture, and propose a new data-driven stopping rule that produces smooth near-maximum likelihood estimates of the mixing density, and simulations demonstrate the quality empirical performance of this estimator.

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BibTeX RIS

Minwoo Chae, Ryan Martin, Stephen G. Walker. 2017-12-11. Fast nonparametric near-maximum likelihood estimation of a mixing density. https://doi.org/10.1016/j.spl.2018.05.012

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