Robust mixture regression using the t-distribution
Robust mixture regression using the t-distribution
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DOI：
10.1016/j.csda.2013.07.019
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发表时间：
2014-03
期刊：
影响因子：
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通讯作者：
W. Yao;Yan Wei;Chun Yu
中科院分区：
文献类型：
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作者：
W. Yao;Yan Wei;Chun Yu
混合回归模型的传统估计是基于分量误差的正态假设，因而对异常值或重尾误差敏感。通过将t分布的混合推广到回归环境，提出了一种基于t分布的稳健混合回归模型。然而，这种新的混合回归模型仍然是不稳健的高杠杆离群值。为了克服这一点，还提出了该方法的修改版本，该方法在自适应地修剪高杠杆点后将基于t分布的混合回归拟合到数据。此外，它提出了自适应选择自由度的t分布使用剖面似然。由于自由度的自适应选择，所提出的稳健混合回归估计具有很高的效率。
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.