We also provide a small simulation study to illustrate the performance of the proposed EM algorithm for finding estimates.
📌 3 Key Takeaways
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Objective & Hypothesis:Finite mixture of multivariate t distributions (Peel and McLachlan, 2000) was introduced as an alternative to the finite mixture of multivariate normal distributions to model datasets with heavy tails.
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Key Findings & Data:Mixture of matrix variate t distributions can provide an alternative robust model to the mixture of matrix variate normal distributions (Viroli, 2011) for modeling matrix variate datasets with heavy tails.
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Conclusion & Impact:We also provide a small simulation study to illustrate the performance of the proposed EM algorithm for finding estimates.
🔬Design:Modeling & Simulation
📍Scope:2000 Verileri
⚙️Variables:Finite mixtures · matrix variate t · matrix variate normal
Abstract
Finite mixture of multivariate t distributions (Peel and McLachlan, 2000) was introduced as an alternative to the finite mixture of multivariate normal distributions to model datasets with heavy tails. In this study, we define the finite mixture of matrix variate t distributions as an extension of finite mixture of multivariate t distributions. Mixture of matrix variate t distributions can provide an alternative robust model to the mixture of matrix variate normal distributions (Viroli, 2011) for modeling matrix variate datasets with heavy tails. We give an Expectation Maximization (EM) algorithm to find the maximum likelihood (ML) estimators for the parameters of interest. We also provide a small simulation study to illustrate the performance of the proposed EM algorithm for finding estimates.
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