Table for Estimating the Goodness of Fit of Empirical Distributions

By N. Smirnov. 1948

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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.”
B-tests: Low Variance Kernel Two-Sample Tests (2013, background): “05 Kolmogorov-Smirnov (KS) normality test [16, 20]), and a Gaussian approximation results in a conservative test threshold (vertical green line).”
Language Model Evaluation Beyond Perplexity (2021, methodology): “The KS test (Smirnov, 1948) is a nonparametric goodness-of-fit test originally designed to assess the fit of a continuous cdf to empirically-observed data; the two-sample version tests whether two samples come from the same distribution.”
Computation of Probability Associated with Anderson–Darling Statistic (2018, unknown): “The most frequently used goodness-of-fit tests are Kolmogorov–Smirnov [3,4], Anderson–Darling [5,6], Pearson’s chi-square [7], Cramér–von Mises [8,9], Shapiro–Wilk [10], Jarque–Bera [11–13], D’Agostino–Pearson [14], and Lilliefors [15,16].”
How True Is Grit? Assessing Its Relations to High School and College Students’ Personality Characteristics, Self-Regulation, Engagement, and Achievement (2017, methodology): “We directly tested the normality assumption for each item response for both samples using Kolmogorov– Smirnov (Smirnov, 1948) and Shapiro-Wilk (Shapiro & Wilk, 1965) tests.”
B-test: A Non-parametric, Low Variance Kernel Two-sample Test (2013, background): “The empirical distribution under H0 on the right does not pass a Komogorov-Smirnov (KS) normality test [13, 17].”
A Brief Review of Tests for Normality (2016, methodology): “The Kolmogorov-Smirnov test was first derived by Kolmogorov (1933) and later modified and proposed as a test by Smirnov (1948).”
Alternative tests for correct specification of conditional predictive densities (2019, unknown): “According to Smirnov (1948) these values are 1.22, 1.36 and 1.63 for α = 0.10, 0.05 and 0.01, respectively.”
Towards Unified INT8 Training for Convolutional Neural Network (2019, methodology): “To further prove this point, we do the Kolmogorov–Smirnov test[50] with distribution parameter solved by maximum likelihood estimation, and report the KS-statistics that consistently reject the assumption that gradients obey any common distribution in Table 1.”
Random projections and goodness-of-fit tests in infinite-dimensional spaces (2006, methodology): “This distribution was tabulated by Smirnov [20] and Massey [14, 15], and is available in most statistical packages.”
Dispersal of Anoplophora glabripennis (Cerambycidae) (2001, methodology): “We also tested for differences in dispersal rate (meters/d) between sexes using the nonparametric Kolmogorov-Smirnov test (Kolmogorov 1941, Smirnov 1948, StatSoft 1999).”
How to form planetesimals from mm-sized chondrules and chondrule aggregates (2015, methodology): “The Kolmogorov-Smirnov (Smirnov 1948; Wall & Jenkins 2003) test gives a standard way to compare F and G.”
Nonparametric Statistical Inference (2020, methodology): “SAS uses internal tables that are similar to those given by D’Agostino and Stephens (1986) to calculate the P value.”
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.”
The California-Kepler Survey. III. A Gap in the Radius Distribution of Small Planets (2017, methodology): “First, we performed a two-sided Kolmogorov-Smirnov (K-S, Kolmogorov 1933; Smirnov 1948) test to assess the probability that the planet radius number distribution for radii in the range 1–3 R⊕ is drawn from a log-uniform distribution.”
Rational Arithmetic Mathematica Functions to Evaluate the One-sided One-sample K-S Cumulative Sample Distribution (2007, methodology): “Numerical Two-sided Approximation by Stephens (1970). Recipes R One-sided For n ≤ 100, direct formula by Smirnov (1944).”