  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 Huber's 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.
