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

Kuo Jung Lee, Martin Feldkircher, Yi Chi Chen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number107180
JournalComputational Statistics and Data Analysis
Volume158
DOIs
Publication statusPublished - 2021 Jun

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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