Statistics, Department of
The R Journal
Date of this Version
12-2021
Document Type
Article
Citation
The R Journal (December 2021) 13(2); Editor: Dianne Cook
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.
Included in
Numerical Analysis and Scientific Computing Commons, Programming Languages and Compilers Commons
Comments
Copyright 2021, The R Foundation. Open access material. License: CC BY 4.0 International