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

Kuo Jung Lee, Martin Feldkircher, Yi Chi Chen

研究成果: Article同行評審

摘要

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.

原文English
文章編號107180
期刊Computational Statistics and Data Analysis
158
DOIs
出版狀態Published - 2021 六月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 計算數學
  • 計算機理論與數學
  • 應用數學

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