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Model averaging is an alternative to model selection for dealing with modeluncertainty, which is widely used and very valuable. However, most of theexisting model averaging methods are proposed based on the least squares lossfunction, which could be very sensitive to the presence of outliers in thedata. In this paper, we propose an outlier-robust model averaging approach byMallows-type criterion. The key idea is to develop weight choice criteria byminimising an estimator of the expected prediction error for the function beingconvex with an unique minimum, and twice differentiable in expectation, ratherthan the expected squared error. The robust loss functions, such as leastabsolute deviation and Huber's function, reduce the effects of large residualsand poor samples. Simulation study and real data analysis are conducted todemonstrate the finite-sample performance of our estimators and compare themwith other model selection and averaging methods.

An outlier-robust model averaging approach by Mallows-type criterion