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These are flexible and parsimonious distributions defined via their quantile function. Bayesian inference is carried out both via a Metropolis-within-Gibbs algorithm and via Stan, also exploiting its variational inference algorithm. In contrast to the Gaussian mixture of factor analyzers model, the proposed model is not a Gaussian mixture, thus being able to describe flexible non-elliptical shapes, which is highlighted via illustrations from simulated data. Quantile-based MFA models are compared in terms of classification accuracy with Gaussian and non-Gaussian MFA on some real datasets, where the extra flexibility of the quantile-based distributed factors clearly helps in achieving good results.<\/jats:p>","DOI":"10.1007\/s00357-025-09515-4","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T04:40:01Z","timestamp":1752208801000},"page":"2-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mixtures of Quantile-Based Factor Analyzers"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6367-6791","authenticated-orcid":false,"given":"Edoardo","family":"Redivo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"9515_CR1","doi-asserted-by":"publisher","unstructured":"Andrews, J.\u00a0L., & McNicholas, P.\u00a0D. 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