Bootstrapped Insights into Empirical Applications of Stochastic Dominance
展示自举法如何通过计算顺序统计量的标准差来揭示累积密度函数尾部估计的不确定性，从而解释为何多种尾部形状都会导致随机占优检验功效显著下降，并证明自举法中的平滑处理能有效提升检验功效。
Bootstrapping, a very versatile statistical technique, significantly amplifies the understanding and success of empirical applications of stochastic dominance. Its ability to calculate the standard deviations of order statistics reveals the uncertainty of the critical estimates of the tails of cumulative density functions. Understanding this uncertainty reveals why a wide variety of tail shapes all cause a notable loss in power for stochastic dominance tests. Simulations show that the smoothing inherent in bootstrapping can significantly increase the power of the tests when dominance exists in the population.