Sulla determinazione empírica di uma legge di distribuzione

By AN Kolmogorov-Smirnov, A. Kolmogorov, M. Kolmogorov. 1933

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Citations in the corpus

A Kernel Test of Goodness of Fit (2016, methodology): “Smirnov, N. Table for estimating the goodness of fit of empirical distributions.”
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing (2015, methodology): “It is well known that the Kolmogorov-Smirnov (KS) test by Kolmogorov [1933] and Smirnov [1948] involves differences in empirical CDFs.”
Revisiting Classifier Two-Sample Tests (2016, methodology): “samples; the Wilcoxon-Mann-Whitney test (Wilcoxon, 1945; Mann & Whitney, 1947), which tests for the difference in rank means of two samples; and the Kolmogorov-Smirnov tests (Kolmogorov, 1933; Smirnov, 1939) and their variants (Kuiper, 1962), which test for the difference in the empirical cumulative”
On Choosing and Bounding Probability Metrics (2002, background): “(4) This metric, due to Kolmogorov (1933), is also called the uniform metric (Zolotarev 1983).”
B-tests: Low Variance Kernel Two-Sample Tests (2013, methodology): “We denote by η̂k(i) the ith empirical estimate MMDu based on a subsample of size B, where 1 ≤ i ≤ nB (for notational purposes, we will index samples as though they are presented in a random fixed order).”
Study of the rare decays of Bs0 and B0 mesons into muon pairs using data collected during 2015 and 2016 with the ATLAS detector (2018, methodology): “A modified Kolmogorov–Smirnov (KS) test [35] is used to estimate the fit quality: the p-value is estimated by comparing the maximum of the KS distance across the four histograms of Figure 8 with the distribution of the same quantity from pseudo-experiments generated with the shape resulting from the”
Comparisons of various types of normality tests (2011, methodology): “The most popular EDF tests are the ones developed by Kolmogorov–Smirnov [22], Cramer–von Mises [23] and Anderson–Darling [24].”
Extropy estimators with applications in testing uniformity (2018, methodology): “Using the observed value of the Kolmogorov–Smirnov statistic (Kolmogorov 1933; Smirnov 1939), Chhikara and Folks (1977) fitted the IG distribution and they concluded that the fit is good (KS test statistic = 0.”
On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests (2015, methodology): “For example, it is well known that the Kolmogorov–Smirnov (KS) test by [20,21] involves differences in empirical CDFs.”
Item Response Theory With Estimation of the Latent Density Using Davidian Curves (2009, methodology): “Normality of each ĝ(y) was evaluated by use of the Kolmogorov-Smirnov (KS; Kolmogorov, 1933; Smirnov, 1939) d statistic, which is the largest distance (in absolute value) between the cumulative distribution functions (CDFs) for the two curves (i.”
Comparing distributions by multiple testing across quantiles or CDF values (2017, methodology): “The KS test (Kolmogorov, 1933; Smirnov, 1948, 1939) is a goodness-of-fit (GOF) test that is only intended to answer question (1) above, but we show that it readily identifies a set of values at which the population and null distribution functions differ in a way that controls FWER.”
Normalization of the Kolmogorov–Smirnov and Shapiro–Wilk tests of normality (2015, background): “Earlier, only the asymptotic distribution had been known (Kolmogorov, 1933; Miller, 1956; Birnbaum, 1952).”
Power comparisons of Shapiro-Wilk , Kolmogorov-Smirnov , Lilliefors and Anderson-Darling tests (2011, background): “Given n ordered data points, . . . , Conover (1999) defined the test statistic proposed by Kolmogorov (1933) as, sup | ∗ | (1) where ‘sup’ stands for supremum which means the greatest.”