Two-Sample Tests for Skewed Data
Adjusting power to assure actual size is within significance level
Bootstrap_t test for two-sample comparisons
Compute t-statistic
Power-adjustment based on non-parametric estimation of the ROC curve
Cornish-Fisher expansion for Welch's t-statistic
Edgeworth expansion for Welch's t-statistic
tcftt: Two-Sample Tests for Skewed Data
The TCFU test
The transformation based test
The classical two-sample t-test works well for the normally distributed data or data with large sample size. The tcfu() and tt() tests implemented in this package provide better type-I-error control with more accurate power when testing the equality of two-sample means for skewed populations having unequal variances. These tests are especially useful when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve a better approximation to the true percentiles. The tt() provides transformations of the Welch's t-statistic so that the sampling distribution become more symmetric. For more technical details, please refer to Zhang (2019) <http://hdl.handle.net/2097/40235>.