A comparative study of computation approaches of the generalized F-test

Yazıcı, Berna; Çavuş, Mustafa

doi:10.1080/02664763.2021.1939660

Cited by 6 publications

(1 citation statement)

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“…Revised Generalized F-Test (RGF) Weerahandi (1995) proposed the Generalized F-test (GF) using the concept of the generalized p-values to test the equality of several normal distributed group means under heteroscedasticity. Yazici and Cavus (2021) discussed the performance the GF test under various conditions. Cavus et al (2017) proposed the modified GF (MGF) test by replacing the maximum likelihood estimators of the sample mean and variance with Huber (1964)'s M-estimators to overcome non-normality caused by outlier(s).…”

Section: Generalized Behrens-fisher Problem In Case Of Non-normalitymentioning

confidence: 99%

A revised generalized F-test for testing the equality of group means under non-normality caused by skewness

Çavuş¹,

Yazıcı²,

Sezer³

2021

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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The non-normality may occur in the data due to several reasons such as the presence of the outlier or skewness. It leads to lose the power and fail control Type I error probability of the tests which are used to test the equality of the group means under heteroscedasticity. To overcome this problem, a revised generalized F-test (RGF) is proposed to test the equality of group means under heteroscedasticity in which non-normality is caused by skewness in this study. An extensive Monte-Carlo simulation study is conducted to investigate and compare the performance of the proposed test with non-parametric alternatives under several values of skewness, and different number of groups. The proposed RGF is the best choice in the high level of skewness for k = 3, 4, 5. The Kruskal-Wallis test shows better performance than the others in small and moderate sample sizes for k = 6, and 7. It is shown that the proposed RGF test is superior than the non-parametric alternatives in the most of the conditions.

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Section: Generalized Behrens-fisher Problem In Case Of Non-normalitymentioning

confidence: 99%