期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 130, 期 -, 页码 98-108出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2013.10.010
关键词
Trimming; Lehmann-Bickel functional; Model diagnostics; Monte Carlo simulations; Power comparison; Robust tests for normality
类别
资金
- Aktion Austria-Czech Republic
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. (C) 2013 Elsevier B.V. All rights reserved.
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