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

Meng Li, Sijia Xiang () and Weixin Yao
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Meng Li: Kansas State University
Sijia Xiang: Zhejiang University of Finance and Economics
Weixin Yao: University of California

Computational Statistics, 2016, vol. 31, issue 4, No 15, 1539-1555

Abstract: Abstract 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.

Keywords: Mixture of linear regression models; Model selection; Robustness; Trimmed likelihood estimator (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-015-0610-x

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