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

被引：1

作者：

Lee, Kuo-Jung ^{[1
,2
]}

Feldkircher, Martin ^{[3
]}

Chen, Yi-Chi ^{[4
]}

机构：

[1] Natl Cheng Kung Univ, Dept Stat, Tainan, Taiwan

[2] Natl Cheng Kung Univ, Inst Data Sci, Tainan, Taiwan

[3] Vienna Sch Int Studies DA, Vienna, Austria

[4] Natl Cheng Kung Univ, Dept Econ, Tainan, Taiwan

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2021年 / 158卷

关键词：

Finite mixture of regression models; Bayesian variable selection; Unknown number of components; High-dimensional data; Financial crisis; BAYESIAN-ANALYSIS; REVERSIBLE JUMP; REGULARIZATION; LASSO; ORDER;

D O I：

10.1016/j.csda.2021.107180

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

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. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：19

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