Testing the Statistical Significance of Linear Programming Estimators
针对线性规划估计（如LINMAP和DEA）缺乏参数显著性检验的问题，提出了两类检验方法：删除变量后模型拟合度变化检验，以及基于非参数刀切法或自助法的标准差/分布检验。模拟表明两类方法均能可靠识别显著与不显著参数。
Linear programming–based estimation procedures are used in a variety of arenas. Two notable areas are multiattribute utility models (LINMAP) and production frontiers (data envelopment analysis (DEA)). Both LINMAP and DEA have theoretical and managerial advantages. For example, LINMAP treats ordinal-scaled preference data as such in uncovering individual-level attribute weights, while regression treats these preferences as interval scaled. DEA produces easy-to-understand efficiency measures, which allow for improved productivity benchmarking. However, acceptance of these techniques is hindered by the lack of statistical significance tests for their parameter estimates. In this paper, we propose and evaluate such parameter significance tests. Two types of tests are forwarded. The first examines whether a model’s fit is significantly reduced when an explanatory variable is deleted. The second is based on generating a standard deviation or distribution for the parameter estimate using nonparametric jackknife or bootstrap techniques. We demonstrate through simulations that both types of tests reliably identify both significant and insignificant parameters. The availability of these tests, especially the relatively simple and easy-to-use tests of the first type, should enhance the utilization of linear programming–based estimation.