A monotonic MM-type algorithm for estimation of nonparametric finite mixture models with dependent marginals

Michael Levine

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arXiv · 2505.16878

A monotonic MM-type algorithm for estimation of nonparametric finite mixture models with dependent marginals

Michael Levine

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Abstract

In this manuscript, we consider a finite nonparametric mixture model with non-independent marginal density functions. Dependence between the marginal densities is modeled using a copula device. Until recently, no deterministic algorithms capable of estimating components of such a model have been available. A deterministic algorithm that is capable of this has been proposed in \citet*{levine2024smoothed}. That algorithm seeks to maximize a smoothed nonparametric penalized log-likelihood; it seems to perform well in practice but does not possess the monotonicity property. In this manuscript, we introduce a deterministic MM (Minorization-Maximization) algorithm for estimation of components of this model that is also maximizing a smoothed penalized nonparametric log-likelihood but that is monotonic with respect to this objective functional. Besides the convergence of the objective functional, the convergence of a subsequence of arguments of this functional, generated by this algorithm, is also established. The behavior of this algorithm is illustrated using both simulated datasets as well as a real dataset. The results illustrate performance that is at least comparable to the earlier algorithm of \citet*{levine2024smoothed}. A discussion of the results and possible future research directions make up the last part of the manuscript.

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Michael Levine. 2025-05-22. A monotonic MM-type algorithm for estimation of nonparametric finite mixture models with dependent marginals. https://arxiv.org/abs/2505.16878

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