A Bayesian Technique for Selecting a Linear Forecasting Model
针对线性回归预测中如何从多个备选变量中选出最佳预测变量集的问题，提出一种贝叶斯方法，它结合数据和专家判断，通过最小化预测变异来选择精确的预测模型，并给出了实证演示。
The specification of a forecasting model is considered in the context of linear multiple regression. Several potential predictor variables are available, but some of them convey little information about the dependent variable which is to be predicted. A technique for selecting the “best” set of predictors which takes into account the inherent uncertainty in prediction is detailed. In addition to current data, there is often substantial expert opinion available which is relevant to the forecasting problem. The approach taken here utilizes both data and expert judgment by incorporating them into a Bayesian predictive distribution. Precise forecasting models are constructed by selecting the set of predictors which minimizes a measure of variability in prediction. An empirical demonstration of the technique is provided.