On robust testing for normality in chemometrics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F14%3A00209193" target="_blank" >RIV/62156489:43110/14:00209193 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.chemolab.2013.10.010" target="_blank" >http://dx.doi.org/10.1016/j.chemolab.2013.10.010</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.chemolab.2013.10.010" target="_blank" >10.1016/j.chemolab.2013.10.010</a>

Result language
angličtina
Original language name
On robust testing for normality in chemometrics
Original language description
The assumption that the data has been generated by a normal distribution underlies many statistical methods used in chemometrics. While such methods can be quite robust to small deviations from normality, for instance caused by a small number of outliers, common tests for normality are not and will often needlessly reject normality. It is therefore better to use tests from the little-known class of robust tests for normality. We illustrate the need for robust normality testing in chemometrics with several examples, review a class of robustified omnibus Jarque-Bera tests and propose a new class of robustified directed Lin-Mudholkar tests. The robustness and power of several tests for normality is compared in a large simulation study. The new tests are robust and have high power in comparison with both classic tests and other robust tests. A new graphical method for assessing normality is also introduced.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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