Regression plays a key role in many research areas and its variable selection is a classic and major problem. This study emphasizes cost of predictors to be purchased for future use, when we select a subset of them. Its economic aspect is naturally formalized by the decision-theoretic approach. In addition, two Bayesian approaches are proposed to address uncertainty about model parameters and models: the restricted and extended approaches, which lead us to rethink about model averaging. From objective, rule-based, or robust Bayes point of view, the former is preferred. Proposed method is applied to three popular datasets for illustration.

翻译：回归在许多研究领域发挥着关键作用,其可变选择是一个经典和主要问题。本研究报告强调,当我们选择一个子集时,为今后使用而购买预测器的成本。其经济方面自然通过决策理论方法正式化。此外,还提出了两种巴伊西亚方法来解决模型参数和模型的不确定性:限制和扩展方法,这使我们重新思考平均模型。从客观、基于规则或稳健的巴耶斯观点看,前者更可取。拟议方法适用于三种流行的数据集,用于说明。