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• We consider the problem of identifying multiple outliers in linear regression models. We propose a penalized trimmed squares (PTS) estimator, where penalty costs for discarding outliers are inserted into the loss function. We propose suitable penalties for unmasking the multiple high-leverage outliers. The robust procedure is formulated as a Quadratic(More)
We consider the problem of identifying multiple outliers in linear regression models. In robust regression the unusual observations should be removed from the sample in order to obtain better fitting for the rest of the observations. Based on the LTS estimate, we propose a penalized trimmed square estimator PTS, where penalty costs for discarding outliers(More)
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