Open AccessA Nonparametric Two-Sample Wald Test of Equality of Variances
Author(s) -
David Allingham,
J. C. W. Rayner
Publication year - 2011
Publication title -
advances in decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.178
H-Index - 13
eISSN - 2090-3367
pISSN - 2090-3359
DOI - 10.1155/2011/748580
Subject(s) - wald test , f test of equality of variances , nonparametric statistics , statistics , mathematics , goldfeld–quandt test , levene's test , sample (material) , estimator , test (biology) , population , normality test , econometrics , sign test , heteroscedasticity , f test , sample size determination , statistical hypothesis testing , z test , test statistic , wilcoxon signed rank test , mann–whitney u test , demography , biology , paleontology , chemistry , homogeneity (statistics) , chromatography , sociology
We develop a test for equality of variances given two independent random samples of observations. The test can be expected to perform well when both sample sizes are at least moderate and the sample variances are asymptotically equivalent to the maximum likelihood estimators of the population variances. The test is motivated by and is here assessed for the case when both populations sampled are assumed to be normal. Popular choices of test would be the two-sample test if normality can be assumed and Levene’s test if this assumption is dubious. Another competitor is the Wald test for the difference in the population variances. We give a nonparametric analogue of this test and call it the test. In an indicative empirical study when both populations are normal, we find that when both sample sizes are at least 25 the test is nearly as robust as Levene’s test and nearly as powerful as the test
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