EDF Statistics for Goodness of Fit and Some Comparisons

By Michael A. Stephens. 1974

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Citations in the corpus

Fast Conditional Independence Test for Vector Variables with Large Sample Sizes (2018, methodology): “Second, they perform a test on the distribution of p -values: their “KS-test p -value” is the p -value of the Kolmogorov-Smirnoff test [Stephens, 1974] for the null hypothesis that the p -values of a CIT are uniformly distributed on a conditionally independent dataset.”
Pricing of zero-coupon and coupon cat bonds (2003, background): “The critical values Cα of the tests given a significance level α (e.g. α = 0.05) can be found in the literature (see e.g. D’Agostino and Stephens 1986, and Stephens 1974).”
Variations of Q–Q Plots: The Power of Our Eyes! (2015, background): “The SW test has been shown to be the most powerful in assessing non-normality (Stephens, 1974; Razali and Wah, 2011).”
Normalization of the Kolmogorov–Smirnov and Shapiro–Wilk tests of normality (2015, methodology): “Using the table of percentage points given by Stephens (1974), we can only conclude that the p-value is larger than 0.”
Testing for homogeneity of variance in time series: Long memory, wavelets, and the Nile River (2002, background): “3 Application to the Nile River water levels As an example of a time series exhibiting both long memory and possible inhomogeneity in variance, we consider the Nile River minimum water level time series (Toussoun (1925); for an interesting recent analysis of this series, see Eltahir and Wang (1999))”
Evaluating the Three Methods of Goodness of Fit Test for Frequency Analysis (2015, background): “In the context of probability statistics, there is a wealth of the literatures on the goodness of fit test, such as Pombo et al. (2015), Durot and Reboul (2010), Goegebeur and Guillou (2010), Lee (2010), Saldana-Zepeda et al. (2010), Stephens (1974a), Stephens (1974b), Woodruff et al. (1984).”
Quantile regression neural networks: Implementation in R and application to precipitation downscaling (2011, methodology): “To verify, the inverse normal cdf is applied to each year of predictive cdf values and adherence to the normal distribution is tested via the Anderson– Darling test (Stephens, 1974).”
Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease. (2013, background): “5 heteroplasmy level), the nonparametric tests have less power (are less likely to give you a small p value), especially with small sample sizes (Stephens 1974).”
Towards Statistically Strong Source Anonymity for Sensor Networks (2008, methodology): “Tabulated values and formulas have been published by Stephens [12].”
Selecting Representative Data Sets (2012, background): “These tests are usually called goodness-of-fit tests and they are widely described in literature [2, 8, 59, 85].”
An empirical power comparison of univariate goodness-of-fit tests for normality (2010, background): “In order to increase its power when μ and σ are estimated from the sample, a modification factor has been proposed for AD [26] resulting in the new statistic AD∗: AD∗ = AD ( 1 + 0.”
Model validation and predictive capability for the thermal challenge problem (2008, methodology): “There are other statistical tests that are also commonly applied in this situation, including the traditional chisquared test and Neyman’s smooth tests [40,41] and references therein.”