A scalable model-averaging method with right censored responses

This paper concerns a scalable model-averaging method with right-censored responses. By using inverse probability censoring weighting to synthesize new responses and a singular value decomposition to transform the original models, this method enables us to estimate the weights by considering maximum of p covariate models. The Mallows criterion and the Jackknife criterion are applied to the selection of weights without the standard constraint that the weights sum to one. The theoretical results show that the calculated weights exhibit asymptotic optimality in the sense of achieving the lowest possible least squares error. Numerical studies demonstrate the excellent averaging performance of the proposed model in terms of both predictive accuracy and computational time.