Economic variable selection
Economic variable selection
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DOI：
10.1002/cjs.11675
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Koji Miyawaki;S. MacEachern
中科院分区：
文献类型：
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作者：
Koji Miyawaki;S. MacEachern
回归在统计学科中起着核心作用，是许多研究领域的主要分析技术。变量选择是回归分析中的一个经典而重要的问题。本文强调变量选择的经济方面。这个问题是用购买预测变量的成本来表示的，这些预测变量是为了将来用途：只有模型中使用的协变量的子集需要购买。这导致了变量选择问题的决策理论公式，其中包括预测因子的成本以及它们的效果。我们采用贝叶斯的观点，并提出了两种方法来解决模型和模型参数的不确定性。这些方法，被称为限制和扩展的方法，导致我们重新思考模型平均。从客观或稳健贝叶斯的角度来看，前者是首选。该方法被应用到三个流行的数据集进行说明。
Regression plays a central role in the discipline of statistics and is the primary analytic technique in many research areas. Variable selection is a classical and major problem for regression. This article emphasizes the economic aspect of variable selection. The problem is formulated in terms of the cost of predictors to be purchased for future use: only the subset of covariates used in the model will need to be purchased. This leads to a decision‐theoretic formulation of the variable selection problems, which includes the cost of predictors as well as their effect. We adopt a Bayesian perspective and propose two approaches to address uncertainty about the model and model parameters. These approaches, termed the restricted and extended approaches, lead us to rethink model averaging. From an objective or robust Bayes point of view, the former is preferred. The proposed method is applied to three popular datasets for illustration.