Abstract
We formulate rank statistics for testing hypotheses in unbalanced, and possibly heteroscedastic, two-factor nested designs with independent observations. These include Wald-type statistics based on the theory introduced by Akritas, Arnold and Brunner, as well as a Box-type approximation which is intended to improve the accuracy of approximation to asymptotic distributions. We also present statistics based on a recent theory of weighted F -statistics for ranks. The actual sizes of the statistics at various nominal levels are compared in a simulation study. Our main conclusion is that the Box-adjusted Wald-type statistic is the only statistic that is accurate across all the situations considered and therefore we recommend it for general use.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
