Nonparametric hypothesis testing for equality of means on the simplex

Michail Tsagris; Simon Preston; Andrew T.A. Wood

doi:10.1080/00949655.2016.1216554

Nonparametric hypothesis testing for equality of means on the simplex

Michail Tsagris^*, Simon Preston, Andrew T.A. Wood

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

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 of data on the simplex. 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.

Original language	English
Pages (from-to)	406-422
Number of pages	17
Journal	Journal of Statistical Computation and Simulation
Volume	87
Issue number	2
DOIs	https://doi.org/10.1080/00949655.2016.1216554
Publication status	Published - 22 Jan 2017
Externally published	Yes

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10.1080/00949655.2016.1216554

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AU - Preston, Simon

AU - Wood, Andrew T.A.

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AB - 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 of data on the simplex. 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.

KW - Compositional data

KW - Hotelling test

KW - James test

KW - bootstrap

KW - empirical likelihood

KW - hypothesis testing

KW - nonparametric

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DO - 10.1080/00949655.2016.1216554

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JF - Journal of Statistical Computation and Simulation

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ER -

Nonparametric hypothesis testing for equality of means on the simplex

Abstract

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