Model averaging for generalized linear models with missing at random covariates

We consider a frequentist model averaging approach for prediction issue of generalized linear model with covariates involving missing at random. To avoid the uncertainty problem from imputation on the missing covariates data, this work adjusts model averaging criterion by selecting a batch of candidate models with the available covariate patterns rather than all possible combinations of covariates. The weights over the batch of candidate models are estimated based on the leave-one-out cross-validation criterion of the fully observed data. The asymptotic optimality of the proposed method is established under some regular conditions. Simulation studies are carried out to demonstrate the finite sample performance of our proposed approach in comparison to other methods. A real data set from NHANES 2017-2018 is also applied to check the effectiveness of the proposed approach.