Salvato in:
| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2012
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/1201.1431 |
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Sommario:
- Goodness-of-fit tests based on the Euclidean distance often outperform chi-square and other classical tests (including the standard exact tests) by at least an order of magnitude when the model being tested for goodness-of-fit is a discrete probability distribution that is not close to uniform. The present article discusses numerous examples of this. Goodness-of-fit tests based on the Euclidean metric are now practical and convenient: although the actual values taken by the Euclidean distance and similar goodness-of-fit statistics are seldom humanly interpretable, black-box computer programs can rapidly calculate their precise significance.