Minimax regret priors for efficiency estimation
提出一种最小遗憾经验先验，用于随机前沿模型中的无效率项及其他参数，先验类由DEA区间得分和最大似然估计导出，蒙特卡洛研究和美国大型银行数据验证了其良好表现。
We propose a minimax regret empirical prior for inefficiencies in a stochastic frontier model and for its other parameters. The class of priors over which we consider minimax regret is given by DEA interval scores and, for the parameters, the class of priors induced by maximum likelihood estimates. The new techniques are shown to perform well in a Monte Carlo study as well as in real data for large U.S. data banks.