Abstract

When a model of main research interest shares partial parameters with several other models, it is of benefit to incorporate the information contained in these other models to improve the estimation and prediction for the main model of interest. Various methods are possible to make use of the additional models as well as the additional observations related to these models. We propose an optimal strategy of doing so in terms of prediction. We develop the model averaging methodology and obtain the optimal weights. We also establish theory to support the method and show its desirable properties both when the main model is correct and when it is incorrect. Numerical experiments including simulation studies and data analysis are conducted to demonstrate the superior performance of our methods.

Joint work with Yizheng Wei and Yanyuan Ma.