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Published in:
Symbolic Formulae for Linear Mixed Models Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Component Elimination Strategies to Fit Mixtures of Multiple Scale Distributions

Authors : Florence Forbes, Alexis Arnaud, Benjamin Lemasson, Emmanuel Barbier

Published in: Statistics and Data Science

Publisher: Springer Singapore

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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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previous chapter Estimating Occupancy and Fitting Models with the Two-Stage Approach
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Metadata
Title
Component Elimination Strategies to Fit Mixtures of Multiple Scale Distributions
Authors
Florence Forbes
Alexis Arnaud
Benjamin Lemasson
Emmanuel Barbier
Copyright Year
2019
Publisher
Springer Singapore
Book
Statistics and Data Science

Print ISBN: 978-981-15-1959-8

Electronic ISBN: 978-981-15-1960-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-15-1960-4_6

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