Parsimonious Finite Mixtures of Matrix-Variate Regressions

Punzo, Antonio; Tomarchio, Salvatore D.

doi:10.1007/978-3-031-13971-0_17

Emerging Topics in Statistics and Biostatistics

2022

DOI: 10.1007/978-3-031-13971-0_17

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Parsimonious Finite Mixtures of Matrix-Variate Regressions

Antonio Punzo

Salvatore D. Tomarchio

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2024

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Cited by 1 publication

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Mixtures of regressions using matrix-variate heavy-tailed distributions

Tomarchio,

Gallaugher

2024

Adv Data Anal Classif

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Finite mixtures of regressions (FMRs) are powerful clustering devices used in many regression-type analyses. Unfortunately, real data often present atypical observations that make the commonly adopted normality assumption of the mixture components inadequate. Thus, to robustify the FMR approach in a matrix-variate framework, we introduce ten FMRs based on the matrix-variate t and contaminated normal distributions. Furthermore, once one of our models is estimated and the observations are assigned to the groups, different procedures can be used for the detection of the atypical points in the data. An ECM algorithm is outlined for maximum likelihood parameter estimation. By using simulated data, we show the negative consequences (in terms of parameter estimates and inferred classification) of the wrong normality assumption in the presence of heavy-tailed clusters or noisy matrices. Such issues are properly addressed by our models instead. Additionally, over the same data, the atypical points detection procedures are also investigated. A real-data analysis concerning the relationship between greenhouse gas emissions and their determinants is conducted, and the behavior of our models in the presence of heterogeneity and atypical observations is discussed.

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Mixtures of regressions using matrix-variate heavy-tailed distributions

Tomarchio,

Gallaugher

2024

Adv Data Anal Classif

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Parsimonious Finite Mixtures of Matrix-Variate Regressions

Cited by 1 publication

References 31 publications

Mixtures of regressions using matrix-variate heavy-tailed distributions

Mixtures of regressions using matrix-variate heavy-tailed distributions

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