Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions

2011/01/01 by B. Arnold Taylor, W. Emerson John · 5 citations
Mathematics · Decision Sciences · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.32614/rj-2011-016

openalex publication_date 2011/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Methodology extending nonparametric goodness-of-fit tests to discrete null distributions has existed for several decades. However, modern statistical software has generally failed to provide this methodology to users. We offer a revision of R's ks.test() function and a new cvm.test() function that fill this need in the R language for two of the most popular nonparametric goodness-of-fit tests. This paper describes these contributions and provides examples of their usage. Particular attention is given to various numerical issues that arise in their implementation.

Citations

• Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
• EDF Statistics for Goodness of Fit and Some Comparisons
• A Comparison of the Pearson Chi-Square and Kolmogorov Goodness-of-Fit Tests with Respect to Validity
• The Kolmogorov-Smirnov Test for Goodness of Fit

Cited by

• Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous
• Hidden scaling patterns and universality in written communication
• Distribution and first insight into the reproductive biology of two species of the genus Rhamdia (Siluriformes, Heptapteridae) in a Biosphere Reserve
• Nested sampling cross-checks using order statistics
• Kolmogorov–Smirnov test [wikipedia]

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