Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jspi.2011.07.023
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dc.titleAn approximate degrees of freedom test for heteroscedastic two-way ANOVA
dc.contributor.authorZhang, J.-T.
dc.date.accessioned2014-10-28T05:09:57Z
dc.date.available2014-10-28T05:09:57Z
dc.date.issued2012-01
dc.identifier.citationZhang, J.-T. (2012-01). An approximate degrees of freedom test for heteroscedastic two-way ANOVA. Journal of Statistical Planning and Inference 142 (1) : 336-346. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jspi.2011.07.023
dc.identifier.issn03783758
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104992
dc.description.abstractHeteroscedastic two-way ANOVA are frequently encountered in real data analysis. In the literature, classical F-tests are often blindly employed although they are often biased even for moderate heteroscedasticity. To overcome this problem, several approximate tests have been proposed in the literature. These tests, however, are either too complicated to implement or do not work well in terms of size controlling. In this paper, we propose a simple and accurate approximate degrees of freedom (ADF) test. The ADF test is shown to be invariant under affine-transformations, different choices of contrast matrix for the same null hypothesis, or different labeling schemes of cell means. Moreover, it can be conducted easily using the usual F-distribution with one unknown degree of freedom estimated from the data. Simulations demonstrate that the ADF test works well in various cell sizes and parameter configurations but the classical F-tests work badly when the cell variance homogeneity assumption is violated. A real data example illustrates the methodologies. © 2011 Elsevier B.V.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jspi.2011.07.023
dc.sourceScopus
dc.subjectApproximate degrees of freedom test
dc.subjectF-test
dc.subjectTests of linear hypotheses
dc.subjectTwo-way ANOVA under heteroscedasticity
dc.subjectWald-type statistic
dc.subjectWishart-approximation
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.jspi.2011.07.023
dc.description.sourcetitleJournal of Statistical Planning and Inference
dc.description.volume142
dc.description.issue1
dc.description.page336-346
dc.description.codenJSPID
dc.identifier.isiut000296115000028
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