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20192026
most citedApproximation by finite mixtures of continuous density functions that vanish at infinity

71 citations · 96 across the 13 of their papers we have counts for

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9 papers · 1 filter

math.ST2025

Approximation rates for finite mixtures of location-scale models and fast least-squares estimators

Hien Duy Nguyen, TrungTin Nguyen, Jacob Westerhout +1

Finite mixture models provide a flexible framework for approximating and estimating multivariate probability densities. We study mixtures formed from translated and rescaled copies…

math.ST2025

Modifications of the BIC for order selection in finite mixture models

Hien Duy Nguyen, TrungTin Nguyen

Finite mixture models are ubiquitous in modern statistical modeling, and a recurring practical issue is choosing the model order. In \citet[Sankhyā Series A, \textbf62, pp. 49--66]…

math.ST2025

On the large-sample limits of some Bayesian model evaluation statistics

Hien Duy Nguyen, Mayetri Gupta, Jacob Westerhout +1

Model selection and order selection problems frequently arise in statistical practice. A popular approach to addressing these problems in the frequentist setting involves informati…

math.ST2021

Non-asymptotic model selection in block-diagonal mixture of polynomial experts models

TrungTin Nguyen, Faicel Chamroukhi, Hien Duy Nguyen +1

Model selection, via penalized likelihood type criteria, is a standard task in many statistical inference and machine learning problems. Progress has led to deriving criteria with…

math.ST2021★ 1 cited

A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi +1

Mixture of experts (MoE) are a popular class of statistical and machine learning models that have gained attention over the years due to their flexibility and efficiency. In this w…

math.ST2020★ 15 cited

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models

Hien Duy Nguyen, TrungTin Nguyen, Faicel Chamroukhi +1

Mixture of experts (MoE) models are widely applied for conditional probability density estimation problems. We demonstrate the richness of the class of MoE models by proving densen…

math.ST2020★ 1 cited

Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts

TrungTin Nguyen, Hien D Nguyen, Faicel Chamroukhi +1

We investigate the estimation properties of the mixture of experts (MoE) model in a high-dimensional setting, where the number of predictors is much larger than the sample size, an…

math.ST2020★ 2 cited

Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces

TrungTin Nguyen, Faicel Chamroukhi, Hien D Nguyen +1

The class of location-scale finite mixtures is of enduring interest both from applied and theoretical perspectives of probability and statistics. We prove the following results: to…

math.ST2019★ 71 cited

Approximation by finite mixtures of continuous density functions that vanish at infinity

T Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi +1

Given sufficiently many components, it is often cited that finite mixture models can approximate any other probability density function (pdf) to an arbitrary degree of accuracy. Un…