Robust mixture regression using the t-distribution

Weixin Yao, Yan Wei and Chun Yu

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 116-127

Abstract: The traditional estimation of mixture regression models is based on the normal assumption of component errors and thus is sensitive to outliers or heavy-tailed errors. A robust mixture regression model based on the t-distribution by extending the mixture of t-distributions to the regression setting is proposed. However, this proposed new mixture regression model is still not robust to high leverage outliers. In order to overcome this, a modified version of the proposed method, which fits the mixture regression based on the t-distribution to the data after adaptively trimming high leverage points, is also proposed. Furthermore, it is proposed to adaptively choose the degrees of freedom for the t-distribution using profile likelihood. The proposed robust mixture regression estimate has high efficiency due to the adaptive choice of degrees of freedom.

Keywords: EM algorithm; Mixture regression models; Outliers; Robust regression; t-distribution (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:116-127

DOI: 10.1016/j.csda.2013.07.019

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