tcftt: tcftt: Two-Sample Tests for Skewed Data

In tcftt: Two-Sample Tests for Skewed Data

Description

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. The approximation is 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>.

tcftt functions

The function 'tcfu()' implements the Cornish-Fisher based two-sample test (TCFU) and 'tt()' implements the transformation based two-sample test (TT). The function 't_edgeworth()' provides the Edgeworth expansion for cumulative distribution function for the Welch's t-statistic, and 't_cornish_fisher()' provides the Cornish-Fisher expansion for the percentiles. The functions 'adjust_power()' and 'pauc()' provide power adjustment for simulation studies so that the actual size of the tests are within the significance level.

tcftt documentation built on July 23, 2020, 5:08 p.m.