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• W2566526665 abstract "Finite mixture regression models have been widely used for modelling mixed regression relationships arising from a clustered and thus heterogenous population. The classical normal mixture model, despite its simplicity and wide applicability, may fail in the presence of severe outliers. Using a sparse, case-specific, and scale-dependent mean-shift mixture model parameterization, we propose a robust mixture regression approach for simultaneously conducting outlier detection and robust parameter estimation. A penalized likelihood approach is adopted to induce sparsity among the mean-shift parameters so that the outliers are distinguished from the remainder of the data, and a generalized Expectation-Maximization (EM) algorithm is developed to perform stable and efficient computation. The proposed approach is shown to have strong connections with other robust methods including the trimmed likelihood method and M-estimation approaches. In contrast to several existing methods, the proposed methods show outstanding performance in our simulation studies." @default.
• W2566526665 created "2017-01-06" @default.
• W2566526665 creator A5006647324 @default.
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• W2566526665 date "2016-12-29" @default.
• W2566526665 modified "2023-10-09" @default.
• W2566526665 title "A new method for robust mixture regression" @default.
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• W2566526665 doi "https://doi.org/10.1002/cjs.11310" @default.
• W2566526665 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5450514" @default.
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