The R Journal: article published in 2021, volume 13:2

RobustBF: An R Package for Robust Solution to the Behrens-Fisher Problem
Gamze Güven, Şükrü Acıtaş, Hatice Şamkar and Birdal Şenoğlu , The R Journal (2021) 13:2, pages 713-733.

Abstract Welch’s two-sample t-test based on least squares (LS) estimators is generally used to test the equality of two normal means when the variances are not equal. However, this test loses its power when the underlying distribution is not normal. In this paper, two different tests are proposed to test the equality of two long-tailed symmetric (LTS) means under heterogeneous variances. Adaptive modified maximum likelihood (AMML) estimators are used in developing the proposed tests since they are highly efficient under LTS distribution. An R package called RobustBF is given to show the implementation of these tests. Simulated Type I error rates and powers of the proposed tests are also given and compared with Welch’s t-test based on LS estimators via an extensive Monte Carlo simulation study.

Received: 2021-07-13; online 2021-12-15, supplementary material, (985 B)
CRAN packages: RobustBF, asht, WRS2
CRAN Task Views implied by cited CRAN packages: Robust

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-107,
  author = {Gamze Güven and Şükrü Acıtaş and Hatice Şamkar and Birdal
          Şenoğlu},
  title = {{RobustBF: An R Package for Robust Solution to the Behrens-
          Fisher Problem}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-107},
  url = {https://doi.org/10.32614/RJ-2021-107},
  pages = {713--733},
  volume = {13},
  number = {2}
}