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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of statistical computation and simulation Ročník 81; číslo 12; s. 2141 - 2155
Hlavní autoři: Yap, B. W., Sim, C. H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 01.12.2011
Taylor & Francis Ltd
Témata:
Comparative studies
Correlation analysis
generalized lambda distribution
Hypothesis testing
kurtosis
Monte Carlo simulation
normality tests
skewness
ISSN:0094-9655, 1563-5163
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie: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