An effective selection of regression variables when the error distribution is incorrectly specified
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 Mallow's \(C_ p\) are investigated.
asymptotically efficient selection of regression variables
scientific article; zbMATH DE number 4044969