Comparisons of various types of normality tests
Normality tests can be classified into tests based on chi-squared, moments, empirical distribution, spacings, regression and correlation and other special tests. This paper studies and compares the power of eight selected normality tests: the Shapiro-Wilk test, the Kolmogorov-Smirnov test, the Lilli...
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| Published in: | Journal of statistical computation and simulation Vol. 81; no. 12; pp. 2141 - 2155 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Abingdon
Taylor & Francis
01.12.2011
Taylor & Francis Ltd |
| Subjects: | |
| ISSN: | 0094-9655, 1563-5163 |
| Online Access: | Get full text |
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| Summary: | Normality tests can be classified into tests based on chi-squared, moments, empirical distribution, spacings, regression and correlation and other special tests. This paper studies and compares the power of eight selected normality tests: the Shapiro-Wilk test, the Kolmogorov-Smirnov test, the Lilliefors test, the Cramer-von Mises test, the Anderson-Darling test, the D'Agostino-Pearson test, the Jarque-Bera test and chi-squared test. Power comparisons of these eight tests were obtained via the Monte Carlo simulation of sample data generated from alternative distributions that follow symmetric short-tailed, symmetric long-tailed and asymmetric distributions. Our simulation results show that for symmetric short-tailed distributions, D'Agostino and Shapiro-Wilk tests have better power. For symmetric long-tailed distributions, the power of Jarque-Bera and D'Agostino tests is quite comparable with the Shapiro-Wilk test. As for asymmetric distributions, the Shapiro-Wilk test is the most powerful test followed by the Anderson-Darling test. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0094-9655 1563-5163 |
| DOI: | 10.1080/00949655.2010.520163 |