Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions
Year: 2026
Type: dissertation
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.
DOI: 10.14288/1.0455556
Author: Fatholahi Dehkordi, Babak
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204562373
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