Model Selection for Exponential Power Mixture Regression Models

Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions for...

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Veröffentlicht in:	Entropy (Basel, Switzerland) Jg. 26; H. 5; S. 422
Hauptverfasser:	Jiang, Yunlu, Liu, Jiangchuan, Zou, Hang, Huang, Xiaowen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.05.2024
Schlagworte:	Algorithms Approximation Consistency exponential power distribution Feature selection finite mixture of linear regression models Lagrange multiplier Maximization Mixtures modified EM algorithm Normality Numerical analysis Optimization Parameter modification Regression models Simulation methods Sparsity Statistical analysis variable selection Variables Iran exponential power distribution finite mixture of linear regression models variable selection modified EM algorithm
ISSN:	1099-4300, 1099-4300
Online-Zugang:	Volltext
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