A simple algorithm for exact multinomial tests
This work proposes a new method for computing acceptance regions of exact multinomial tests. From this an algorithm is derived, which finds exact p-values for tests of simple multinomial hypotheses. Using concepts from discrete convex analysis, the method is proven to be exact for various popular te...
Gespeichert in:
| 1. Verfasser: | |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2023
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| In: |
Journal of computational and graphical statistics
Year: 2023, Jahrgang: 32, Heft: 2, Pages: 539-550 |
| ISSN: | 1537-2715 |
| DOI: | 10.1080/10618600.2022.2102026 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1080/10618600.2022.2102026
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| Verfasserangaben: | Johannes Resin |
MARC
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| 520 | |a This work proposes a new method for computing acceptance regions of exact multinomial tests. From this an algorithm is derived, which finds exact p-values for tests of simple multinomial hypotheses. Using concepts from discrete convex analysis, the method is proven to be exact for various popular test statistics, including Pearson’s Chi-square and the log-likelihood ratio. The proposed algorithm improves greatly on the naive approach using full enumeration of the sample space. However, its use is limited to multinomial distributions with a small number of categories, as the runtime grows exponentially in the number of possible outcomes. The method is applied in a simulation study, and uses of multinomial tests in forecast evaluation are outlined. Additionally, properties of a test statistic using probability ordering, referred to as the “exact multinomial test” by some authors, are investigated and discussed. The algorithm is implemented in the accompanying R package ExactMultinom. Supplementary materials for this article are available online. | ||
| 650 | 4 | |a Acceptance regions | |
| 650 | 4 | |a Goodness-of-fit test | |
| 650 | 4 | |a Log-likelihood ratio | |
| 650 | 4 | |a Pearson’s Chi-square | |
| 650 | 4 | |a Probability mass statistic | |
| 650 | 4 | |a R software | |
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