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Outlier detection and robust mixture modeling using nonconvex penalized likelihood
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https://doi.org/10.1016/j.jspi.2015.03.003Abstract
Finite mixture models are widely used in a variety of statistical applications. However, the classical normal mixture model with maximum likelihood estimation is prone to the presence of only a few severe outliers. We propose a robust mixture modeling approach using a mean-shift formulation coupled with nonconvex sparsity-inducing penalization, to conduct simultaneous outlier detection and robust parameter estimation. An efficient iterative thresholding-embedded EM algorithm is developed to maximize the penalized log-likelihood. The efficacy of our proposed approach is demonstrated via simulation studies and a real application on Acidity data analysis.
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