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Geometric aspects of robust testing for normality and sphericity

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journal contribution
posted on 09.01.2017, 11:03 by Wolf-Dieter Richter, Lubos Strelec, Hamid AhmadinezhadHamid Ahmadinezhad, Milan Stehlik
Stochastic Robustness of Control Systems under random excitation motivates challenging developments in geometric approach to robustness. The assumption of normality is rarely met when analyzing real data and thus the use of classic parametric methods with violated assumptions can result in the inaccurate computation of pvalues, e↵ect sizes, and confidence intervals. Therefore, quite naturally, research on robust testing for normality has become a new trend. Robust testing for normality can have counter-intuitive behavior, some of the problems have been introduced in [46]. Here we concentrate on explanation of small-sample e↵ects of normality testing and its robust properties, and embedding these questions into the more general question of testing for sphericity. We give geometric explanations for the critical tests. It turns out that the tests are robust against changes of the density generating function within the class of all continuous spherical sample distributions.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

Stochastic Analysis and Applications

Volume

35

Issue

3

Citation

RICHTER, W-D. ...et al., 2017. Geometric aspects of robust testing for normality and sphericity. Stochastic Analysis and Applications, 35 (3), pp. 511-532.

Publisher

© Taylor & Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

14/12/2016

Publication date

2017-02-06

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in Stochastic Analysis and Applications on 06 Feb 2017, available online: http://dx.doi.org/10.1080/07362994.2016.1273785

ISSN

1532-9356

eISSN

1532-9356

Language

en