Prediction Using Many Samples with Models Possibly Containing Partially Shared Parameters
利用可能部分共享参数的多样本模型进行预测
We consider prediction based on a main model. When the main model shares partial parameters with several other helper models, we make use of the additional information. Specifically, we propose a Model Averaging Prediction (MAP) procedure that takes into account data related to the main model as well as data related to the helper models. We allow the data related to different models to follow different structures, as long as they share some common covariate effect. We show…