A SIMULATION STUDY ON TESTS FOR ONE-WAY ANOVA UNDER THE UNEQUAL VARIANCE ASSUMPTION

Year 2010, Volume: 59 Issue: 2, 15 - 34, 01.08.2010

Esra Yiğit Fikri Gökpınar

https://doi.org/10.1501/Commua1_0000000660

Cited By: 5

Abstract

The classical F-test to compare several population means depends
on the assumption of homogeneity of variance of the population and the normality. When these assumptions especially the equality of variance is dropped,
the classical F-test fails to reject the null hypothesis even if the data actually provide strong evidence for it. This can be considered a serious problem
in some applications, especially when the sample size is not large. To deal
with this problem, a number of tests are available in the literature. In this
study, the Brown-Forsythe, Weerahandiís Generalized F, Parametric Bootstrap, Scott-Smith, One-Stage, One-Stage Range, Welch and Xu-Wangís Generalized F-tests are introduced and a simulation study is performed to compare
these tests according to type-1 errors and powers in different combinations of
parameters and various sample sizes.

Keywords

Brown-Forsythe test, Generalized F-test, Parametric Bootstrap test

A SIMULATION STUDY ON TESTS FOR ONE-WAY ANOVA UNDER THE UNEQUAL VARIANCE ASSUMPTION

Abstract

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