Preliminary tests when comparing means

I. Parra-Frutos ()
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I. Parra-Frutos: University of Murcia

Computational Statistics, 2016, vol. 31, issue 4, No 19, 1607-1631

Abstract: Abstract The aim of this paper is to find a procedure to test equal means that is robust at the significance level. A simulation study is conducted to compare the performance of different strategies, including unconditionally applying the bootstrap ANOVA and twelve adaptive tests that take in pre-testing normality, homoscedasticity and skewness. Various final tests of equal means have been considered, like the ANOVA, bootstrap ANOVA, Welch, Brown–Forsythe and bootstrap James test. Our simulation results reveal that the usual adaptive test used by applied researchers (based on testing normality and homoscedasticity to choose from the ANOVA, Welch and Kruskal–Wallis tests) performs poorly. The simulation results show that preliminary tests may improve the performance of a test, and that this depends on the pre-tests chosen. In particular, we find that using decisions on normality to select the right homoscedasticity test and then choosing between the Brown–Forsythe test and the bootstrap ANOVA leads to controlling the Type I error rate in all of the settings studied.

Keywords: Bootstrap ANOVA; Adaptive tests; Tests of equal means; Behrens–Fisher problem; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00180-016-0656-4

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