Weighted likelihood latent class linear regression

Luca Greco (), Antonio Lucadamo () and Claudio Agostinelli ()
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Luca Greco: University of Sannio
Antonio Lucadamo: University of Sannio
Claudio Agostinelli: University of Trento

Statistical Methods & Applications, 2021, vol. 30, issue 2, No 13, 746 pages

Abstract: Abstract 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.

Keywords: Classification; EM; Mixture; Outliers detection; Pearson residuals; Regression; Robustness; Weighted likelihood (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10260-020-00540-8

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