Weighted least squares model averaging for accelerated failure time models

This paper proposes a new model averaging method for the accelerated failure time models with right censored data. A weighted least squares procedure is used to estimate the parameters of candidate models. In this procedure, the candidate models are not required to be nested, and the weights selected by Mallows criterion are not limited to be discrete, which make the proposed method very flexible and general. The asymptotic optimality of the proposed method is proved under some mild conditions. Particularly, it is shown that the optimality remains valid even when the variances of the error terms are estimated and the feasible weighted least squares estimators are averaged. Simulation studies show that the proposed method has better prediction performance than many popular model selection or model averaging methods when all candidate models are misspecified. Finally, an application about primary biliary cirrhosis is provided.