Adaptive bootstrap tests and their competitors in the c-sample location problem
This paper deals with a power comparison of different types of tests, parametric, nonparametric, robustified and adaptive ones for the two-sided c-sample location problem. A robustness study on level alpha in the case of heteroscedasticity and non-normal distributions is included in our study, too. First of all, we consider an adaptive test based on Hogg's concept and two adaptive Bootstrap tests using Hogg's principle. It turns out that the adaptive Hogg-test is the best one in the case of homoscedasticity but for heteroscedasticity, an adaptive Bootstrap test using Hogg's principle is preferable.
bootstrap power comparison parametric nonparametric sampling strategies robustified and adaptive tests tailweight skewness nonnormality alpha-robustness data example