Component elimination strategies to fit mixtures of multiple scale distributions

Florence Forbes 1 Alexis Arnaud 1 Benjamin Lemasson 2 Emmanuel Barbier 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIN - Grenoble Institut des Neurosciences
Abstract : We address the issue of selecting automatically the number of components in mixture models with non-Gaussian components. As a more efficient alternative to the traditional comparison of several model scores in a range, we consider procedures based on a single run of the inference scheme. Starting from an overfitting mixture in a Bayesian setting, we investigate two strategies to eliminate superfluous components. We implement these strategies for mixtures of multiple scale distributions which exhibit a variety of shapes not necessarily elliptical while remaining analytical and tractable in multiple dimensions. A Bayesian formulation and a tractable inference procedure based on variational approximation are proposed. Preliminary results on simulated and real data show promising performance in terms of model selection and computational time.
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Submitted on : Monday, December 16, 2019 - 9:56:22 PM
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Florence Forbes, Alexis Arnaud, Benjamin Lemasson, Emmanuel Barbier. Component elimination strategies to fit mixtures of multiple scale distributions. RSSDS 2019 - Research School on Statistics and Data Science, Jul 2019, Melbourne, Australia. pp.1-15. ⟨hal-02415090⟩

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