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Geometric aspects of robust testing for normality and sphericity
journal contributionposted on 2017-01-09, 11:03 authored by Wolf-Dieter Richter, Lubos Strelec, Hamid Abban, 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 . 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.
- Mathematical Sciences
Published inStochastic Analysis and Applications
CitationRICHTER, 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
- AM (Accepted Manuscript)
Publisher statementThis 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/
NotesThis 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