Tests for mean equality that do not require homogeneity of variances: do they really Work?

DOI10.1080/03610910008813644zbMath1100.62514OpenAlexW1988324784MaRDI QIDQ4784251

Rand R. Wilcox, Jason Taylor, Rhonda K. Kowalchuk, H. J. Keselman

Publication date: 10 December 2002

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910008813644

zbMATH Keywords

Monte CarloNonnormalityRobust EstimatorsTests for Mean EqualityVariance Heterogeneity

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Theoretical considerations when simulating data from the g‐and‐h family of distributions ⋮ Comparison of several means: a fiducial based approach ⋮ Heterogeneity of variance and biased hypothesis tests

Cites Work

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• Robust nonorthogonal analyses revisited: An update based on trimmed means
• A one-way random effects model for trimmed means
• Confidence Interval Robustness with Long-Tailed Symmetric Distributions
• Exact Analysis of Variance with Unequal Variances: Test Procedures and Tables
• Size performance of some tests in one-way anova
• Single-stage analysis of variance under heteroscedasticity
• The 'improved' brown and forsythe test for mean equality: some things can't be fixed
• ANOVA: The practical importance of heteroscedastic methods, using trimmed means versus means, and designing simulation studies
• Some Results on the Tukey‐Mclaughlin and Yuen Methods for Trimmed Means when Distributions are Skewed
• The Small Sample Behavior of Some Statistics Which Test the Equality of Several Means
• THE SIGNIFICANCE OF THE DIFFERENCE BETWEEN TWO MEANS WHEN THE POPULATION VARIANCES ARE UNEQUAL

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