Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions
Abstract
We propose a multivariate extension of the pseudo-Voigt profile-a weighted convex combination of Gaussian and Cauchy distributions-within a finite mixture modeling framework for robust model-based clustering and outlier detection. To ensure parsimony and coherence within clusters, shared location and scale parameters are imposed between the Gaussian and Cauchy components. Parameter estimation is carried out via an Expectation Maximization algorithm, with latent variables facilitating efficient likelihood-based inference. The performance of the proposed model is evaluated through simulation studies and applications to real-world data. Comparisons with established robust models, including mixtures of contaminated normal distributions, are provided to illustrate the model's clustering accuracy and outlier detection capabilities. The framework is shown to be particularly effective for data characterized by heavy-tailed behavior.
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BibTeX
@misc{dehkordi2026robust,
title = {{Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions}},
author = {Babak F. Dehkordi and Jeffrey L. Andrews and Andrew Jirasek},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.27606},
eprint = {2608.27606},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.27606},
}
RIS
TY - UNPB
TI - Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions
AU - Babak F. Dehkordi
AU - Jeffrey L. Andrews
AU - Andrew Jirasek
PY - 2026
DA - 2026/08/27
PB - arXiv
DO - 10.48550/arxiv.2608.27606
UR - https://arxiv.org/abs/2608.27606
AB - We propose a multivariate extension of the pseudo-Voigt profile-a weighted convex combination of Gaussian and Cauchy distributions-within a finite mixture modeling framework for robust model-based clustering and outlier detection. To ensure parsimony and coherence within clusters, shared location and scale parameters are imposed between the Gaussian and Cauchy components. Parameter estimation is carried out via an Expectation Maximization algorithm, with latent variables facilitating efficient likelihood-based inference. The performance of the proposed model is evaluated through simulation studies and applications to real-world data. Comparisons with established robust models, including mixtures of contaminated normal distributions, are provided to illustrate the model's clustering accuracy and outlier detection capabilities. The framework is shown to be particularly effective for data characterized by heavy-tailed behavior.
ER -