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Summary A stochastic complexity approach for model selection in robust linear regression is studied in this paper. Computational aspects and applications of this approach are the focuses of the study. Particularly, we provide both procedures and a package of S language programs for computing the stochastic complexity and for proceeding with the associated model selection. On the other hand, we discuss...
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