Comparisons of various types of normality tests

• 發表在: Journal of Statistical Computation and Simulation
• 主要作者: 2-s2.0-84857935932
• 格式: Article
• 語言: English
• 出版: 2011
• 在線閱讀: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857935932&doi=10.1080%2f00949655.2010.520163&partnerID=40&md5=6fc73b3362f4cd0d5e1a22f5cf9bb3d9

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. © 2011 Copyright Taylor and Francis Group, LLC.