| 简介： | We develop improved statistical procedures for testing stochastic monotonicity. While existing tests rely on the least favorable models, we use data dependent critical values to raise the limiting rejection rate of the test to the nominal significance level over a wide region of the null hypothesis. This improves power against relevant local alternatives. To show the validity of our approach we draw on recent results on the directional differentiability of the least concave majorant operator, and on bootstrap inference when smoothness conditions sufficient to apply the functional delta method for the bootstrap are not satisfied. |