Nonparametric hypothesis testing for equality of means on the simplex
Michail Tsagris, Simon Preston, Andrew T.A. Wood
TL;DR
This paper evaluates nonparametric tests for equality of means on the simplex, recommending bootstrap-calibrated James statistic based on extensive simulations and practical considerations.
Contribution
It introduces and compares empirical likelihood, exponential empirical likelihood, Hotelling, and James tests for simplex data, highlighting the effectiveness of bootstrap calibration for the James statistic.
Findings
Bootstrap-calibrated James statistic performs well in simulations.
Empirical likelihood methods are effective but computationally intensive.
The study provides practical guidance for testing mean equality on the simplex.
Abstract
In the context of data that lie on the simplex, we investigate use of empirical and exponential empirical likelihood, and Hotelling and James statistics, to test the null hypothesis of equal population means based on two independent samples. We perform an extensive numerical study using data simulated from various distributions on the simplex. The results, taken together with practical considerations regarding implementation, support the use of bootstrap-calibrated James statistic.
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Taxonomy
TopicsAdvanced Statistical Methods and Models · Survey Sampling and Estimation Techniques · Statistical Methods and Bayesian Inference