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Weighted likelihood latent class linear regression

机译：加权似然潜在类线性回归

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摘要

A weighted likelihood approach for robust fitting of a finite mixture of linear regression models is proposed. An EM type algorithm and its variant based on the classification likelihood have been developed. The proposed algorithm is characterized by an M-step that is enhanced by the computation of weights aimed at downweighting outliers. The weights are based on the Pearson residuals stemming from the assumption of normality for the error distribution. Formal rules for robust clustering and outlier detection are also defined based on the fitted mixture model. The behavior of the proposed methodologies has been investigated by numerical studies and real data examples in terms of both fitting and classification accuracy and outlier detection.
机译：提出了一种加权似然方法对线性回归模型的有限混合物的鲁棒拟合。 已经开发了一种基于分类似然的EM型算法及其变体。 所提出的算法的特征在于由瞄准潜行异常值的权重的计算来增强的M-Step。 权重基于误差分布的正常性源的Pearson残差。 还基于拟合混合物模型定义了鲁棒群集和异常值检测的正式规则。 在拟合和分类准确度和异常检测方面，通过数值研究和实际数据示例研究了所提出的方法的行为。

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