On robust testing for normality in chemometrics
2014 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 130, 98-108 p.Article in journal (Refereed) Published
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 are 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.
Place, publisher, year, edition, pages
2014. Vol. 130, 98-108 p.
Trimming, Lehmann-Bickel functional, Model diagnostics, Monte Carlo simulations, Power comparison, Robust tests for normality
IdentifiersURN: urn:nbn:se:uu:diva-220303DOI: 10.1016/j.chemolab.2013.10.010ISI: 000330914900014OAI: oai:DiVA.org:uu-220303DiVA: diva2:705535