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A New Procedure of Multivariate Multiple Comparisons

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Pietrzykowski R., Zieliński W., Department of Mathematical Statistics and Experimentation U. o. A. o. W.

Acta Universitatis Lodziensis. Folia Oeconomica

2004

vol. 175

Advances on Permutation Multivariate Analysis of Variance for big data

100%

Bonnini S., Assegie G. M.

Statistics in Transition new series

2022

vol. 23

issue 2

163-183

In many applications of the multivariate analyses of variance, the classic parametric solutions for testing hypotheses of equality in population means or multisample and multivariate location problems might not be suitable for various reasons. Multivariate multisample location problems lack a comparative study of the power behaviour of the most important combined permutation tests as the number of variables diverges. In particular, it is useful to know under which conditions each of the different tests is preferable in terms of power, how the power of each test increases when the number of variables under the alternative hypothesis diverges, and the power behaviour of each test as the function of the proportion of true alternative hypotheses. The purpose of this paper is to fill the gap in the literature about combined permutation tests, in particular for big data with a large number of variables. A Monte Carlo simulation study was carried out to investigate the power behaviour of the tests, and the application to a real case study was performed to show the utility of the method.

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1 Acta Universitatis Lodziensis. Folia Oeconomica

1 Statistics in Transition new series

1 Assegie G. M.

1 Bonnini S.

1 Department of Mathematical Statistics and Experimentation U. o. A. o. W.

1 Pietrzykowski R.

1 Zieliński W.

1 2022

1 2004

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A New Procedure of Multivariate Multiple Comparisons

Advances on Permutation Multivariate Analysis of Variance for big data