Model averaging is an alternative to model selection for dealing with model uncertainty, which is widely used and very valuable. However, most of the existing model averaging methods are proposed based on the least squares loss function, which could be very sensitive to the presence of outliers in the data. In this paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The key idea is to develop weight choice criteria by minimising an estimator of the expected prediction error for the function being convex with an unique minimum, and twice differentiable in expectation, rather than the expected squared error. The robust loss functions, such as least absolute deviation and Hubers function, reduce the effects of large residuals and poor samples. Simulation study and real data analysis are conducted to demonstrate the finite-sample performance of our estimators and compare them with other model selection and averaging methods.
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