Variable selection in finite mixture of regression models with an unknown number of components

Kuo-Jung Lee, Martin Feldkircher and Yi-Chi Chen

Computational Statistics & Data Analysis, 2021, vol. 158, issue C

Abstract: A Bayesian framework for finite mixture models to deal with model selection and the selection of the number of mixture components simultaneously is presented. For that purpose, a feasible reversible jump Markov Chain Monte Carlo algorithm is proposed to model each component as a sparse regression model. This approach is made robust to outliers by using a prior that induces heavy tails and works well under multicollinearity and with high-dimensional data. Finally, the framework is applied to cross-sectional data investigating early warning indicators. The results reveal two distinct country groups for which estimated effects of vulnerability indicators vary considerably.

Keywords: Finite mixture of regression models; Bayesian variable selection; Unknown number of components; High-dimensional data; Financial crisis (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000141

DOI: 10.1016/j.csda.2021.107180

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