Comparison of several independent population means when their samples contain log-normal and possibly zero observations.
In this paper, we consider the problem of testing the mean equality of several independent populations that contain log-normal and possibly zero observations. We first showed that the currently used methods in statistical practice, including the nonparametric Kruskal-Wallis test, the standard ANOVA F-test and its two modified versions, the Welch test and the Brown-Forsythe test, could have poor Type I error control. Then we propose a likelihood ratio test that is shown to have much better Type I error control than the existing methods. Finally, we analyze two real data sets that motivated our study using the proposed test.
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Department of Medicine, Indiana University School of Medicine, Indianapolis 46202-2859, USA. zhou@mako.biostat.iupui.edu
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D001699:Biometry; D016527:Drug Costs / Q000706:statistics & numerical data; D017723:Drug Utilization Review / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D009010:Monte Carlo Method; D010349:Patient Compliance / Q000706:statistics & numerical data; D011446:Prospective Studies; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data