Component elimination strategies to fit mixtures of multiple scale distributions - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Component elimination strategies to fit mixtures of multiple scale distributions

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.
Fichier principal
Vignette du fichier
CompElim.pdf (1.33 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02415090 , version 1 (16-12-2019)

Identifiers

Cite

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.81-95, ⟨10.1007/978-981-15-1960-4_6⟩. ⟨hal-02415090⟩
83 View
243 Download

Altmetric

Share

Gmail Facebook X LinkedIn More