Robust estimation of the number of components for mixtures of linear regression models

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• 作者：Meng Li ; Sijia Xiang ; Weixin Yao
• 关键词：Mixture of linear regression models ; Model selection ; Robustness ; Trimmed likelihood estimator
• 刊名：Computational Statistics
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：31
• 期：4
• 页码：1539-1555
• 全文大小：552 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Statistics
  Statistics
  Probability and Statistics in Computer Science
  Probability Theory and Stochastic Processes
  Economic Theory
  
• 出版者：Physica Verlag, An Imprint of Springer-Verlag GmbH
• ISSN：1613-9658
• 卷排序：31

文摘

In this paper, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criteria. Compared to the traditional information criteria, the trimmed criteria are robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. Two real data applications are also used to illustrate the effectiveness of the trimmed model selection methods.