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Comparison of power of three tests of equality of means of Pareto populations

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  1. Abstract
  2. The three tests under consideration
    1. The ANOVA test
    2. The likelihood ratio test
    3. The Kruskal-Wallis test
  3. Power functions of the tests
  4. Comparison of the power simulation results
    1. Simulation method
    2. Results of the power comparison
  5. Conclusions and recommendations
  6. References

To test the equality of means of non-normal data, both parametric test and non-parametric test may be applied. In this paper, the analysis of variance (ANOVA) test, the likelihood ratio test and the Kruskal-Wallis test are investigated in order to test the means of several Pareto distributions. Since Pareto data are non-normal, they must be transformed to normal with homogeneous variances so that ANOVA test can be applied. The alternative transformation for any sets of Pareto data to normal with equal variances is used. The power of the ANOVA test, the likelihood ratio test and the Kruskal-Wallis test are compared. It is found that the power depends on the location and the shape parameters. The results are divided into three cases. First, if the location parameters are the same but the shape parameters are different, for samples of sizes 10 to 30, the power of the likelihood ratio test is the highest. The power of all the tests is almost the same when the population means are largely different. Second, if both the location parameters and the shape parameters are different, for samples of sizes 10, the power of the likelihood ratio test is still the highest. For samples of sizes 20 to 30, the power of the ANOVA test is the highest. Moreover, the power of all tests are almost the same when the population means are much different. Last, with the different location parameters but the same shape parameters, for samples of sizes 10 to 30, the power of the Kruskal-Wallis test is the highest. The power of the Kruskal-Wallis test and the likelihood ratio test are almost equal when the differences of the population means are large, but the power of ANOVA test is still the lowest. For unequal sample sizes, the conclusion is the same as the case of samples of sizes 10. Keywords: Pareto data, Power, The alternative transformation

[...] The Three Tests under Consideration There are many methods for testing the equality of parameters in several populations, most of which can be applied to test for equality of just one parameter, while the others are held fixed. If the data are non-normal, we usually apply the likelihood ratio test to scale, location, or shape parameter. Nagarsenker derived the exact distribution of the likelihood ratio statistic for testing the equality of k one parameter exponential populations. Chaudhuri and Chandra presented an alternative procedure for testing the equality of scale parameters of Weibull populations with a common shape based on sample quantiles. [...]


[...] The power of all three tests are almost the same if the differences among the population means are large If the populations have different values of both location and shape parameters, the likelihood ratio test has the highest power for the simulations with equal samples of sizes 10 and unequal sample sizes but ANOVA test is higher than the likelihood ratio test a little for the simulations with equal samples of sizes 20 and 30. However, all the three tests have almost the same power when the differences among the population means are large. [...]


[...] ; A Simulation Comparison of Several Procedures for Testing the Poisson Assumption, The Statistician, Vol pp 365- Kruskal, W. H.; A Nonparametric Test for the Several Sample Problem, The Annals of Mathematical Statistics, Vol. pp 525- Nagarsenker, P. B.; On A Test of Equality of Several Exponential Survival Distributions, Biometrika, Vol. pp 475- Patnaik, P.B.; The Non-Central ? and F-Distributions and Their Applications, Biometrika, Vol pp 202- Posten, H.O. and Bargemann, R.E.; Power of the Likelihood-Ratio Test of the General Linear Hypothesis in Multivariate Analysis, Biometrika, Vol. [...]

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