Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions

By Taylor Baillie Arnold, John W. Emerson. The R Journal, 2011

324 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/af3206aac2d260d880994a0561972b95b7df78f3

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In Statistics and Probability

1. Subrata Chakraborty
2. D. Chakravarty

Citations in the corpus

• A new discrete probability distribution with integer support on (−∞, ∞) (2016, methodology): “It may be noted that for the Kolmogorov–Smirnov (KS) test, the test statistic is computed directly from definition and only the p value is computed using ks.test () function of package dgof (Arnold and Emerson, 2011)) of R.”