An effective selection of regression variables when the error distribution is incorrectly specified
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An asymptotically efficient selection of regression variables is considered in the situation where the statistician estimates regression parameters by the maximum likelihood method but fails to choose a likelihood function matching the true error distribution. The proposed procedure is useful when a robust regression technique is applied but the data in fact do not require that treatment. Examples and a Monte Carlo study are presented and relationships to other selectors such as Mallows'Cp are investigated.
Key words and phrasesVariable selection regression analysis robust regression model choice
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- Li, K. C. (1984). Asymptotic optimality forCp,C1, cross-validation and generalized cross-validation: Discrete, index set, Manuscript.Google Scholar