Testing for Normality

By Karen Kafadar. 2003

893 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2aca8a96722f5a26b5f22308586eaec1ebbbc56b

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In Statistics and Probability

Citations in the corpus

A Generalization of Shapiro–Wilk's Test for Multivariate Normality (2009, background): “See also the reviews by Mecklin and Mundfrom (2005), Srivastava and Mudholkar (2003), Thode (2002), and Henze (2002).”
Normalidade de variáveis: métodos de verificação e comparação de alguns testes não-paramétricos por simulação Normality of variables: diagnosis methods and comparison of some nonparametric tests by simulation (2012, background): “Mais detalhes sobre estes testes podem ser encontrados em (6) e (7).”
Invariant tests for multivariate normality: a critical review (2002, background): “for assessing univariate normality ( Geary (1947) ).”
Finding Optimal Normalizing Transformations via bestNormalize (2021, methodology): “The bestNormalize package utilizes nortest (Gross and Ligges, 2015) to compute this statistic; more information on its computation and degrees of freedom can be found in D’Agostino (1986) and Thode (2002).”
Testing the Residuals of an ARIMA Model on the Çekerek Stream Watershed in Turkey (2005, background): “The AD test for normality has the functional form (Thode, 2002)”
An empirical power comparison of univariate goodness-of-fit tests for normality (2010, background): “Nonetheless, studies have shown that, even for a sample size of 1000, the percentiles of this new standard statistic do not converge to those of the standard normal distribution and exhibit asymmetric behaviour [34].”
Testing exponentiality based on characterizations of the exponential distribution (2011, background): “The goodness-of-fit tests have been discussed by many authors including D’Agostino and Stephens [1], Huber-Carol et al. [2], Li and Papadopoulos [3], Thode [4], Henze and Meintanis [5,6], Henze et al. [7], Zhang and Cheng [8], Meintanis [9], Steele and Chaseling [10], Meintanis [11], Jager and Welln”
Contributions of empirical and quantile processes to the asymptotic theory of goodness-of-fit tests (2000, methodology): “Other tests of normality are the u-test (see David, Hart ley and Pearson 1954), based on the ratio between the range and the s tandard deviation in the sample, and the ~.-test (see Geary 1947), which studies the ratio of the sample mean to the s tandard deviation.”
A Brief Review of Tests for Normality (2016, background): “By the middle of this century Geary (1947) made this comment `Normality is a myth; there never was and never will be a normal distribution.”
Short-Term Load Forecasting Based on a Semi-Parametric Additive Model (2012, methodology): “normality of the model residuals are tested using the AndersonDarling test and Pearson chi-square test [23].”
The acceptance and use of a virtual learning environment in China (2008, background): “Four cases had to be excluded from data analysis, because they showed a combination of a large number of high z -scores on the one hand and high bivariate and multivariate Mahalanobis Distances on the other hand (cf. Thode, 2002).”
NOTES AND CORRESPONDENCE A Cautionary Note on the Use of the Kolmogorov-Smirnov Test for Normality (2007, methodology): “We have used the Jarque–Bera (JB), Shapiro–Wilk (SW), Anderson–Darling (AD), Cramer–von Mises (CVM), Pearson chi-square (PCH), and Shapiro–Francio (SF) [for references see Thode (2002)].”
Impact of uncertainty on the optimum nitrogen fertilization rate and agronomic, ecological and economic factors in an oilseed rape based crop rotation (2007, methodology): “According to a Shapiro–Wilk test (P=0.05; Thode 2002), the residuals of the Q model were normally distributed in 16 of 19, 15 of 19 and 14 of 19 crop and year combinations for the Q, LRP and QRP model respectively (data not shown).”