方法对比
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| 稳健 Fisher 精确检验× | 稳健卡方检验× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 1935 (base); mid-p robustification 1961+ | 1984 (power divergence); 1900 (Pearson baseline) |
| 提出者≠ | Fisher (1935); mid-p extension by Lancaster (1961) and others | Cressie & Read (power divergence framework); Pearson chi-square extended by multiple authors |
| 类型≠ | Robust exact conditional test | Robust categorical association / goodness-of-fit test |
| 开创性文献≠ | Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933 | Cressie, N., & Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society: Series B, 46(3), 440–464. DOI ↗ |
| 别名 | mid-p Fisher's exact test, robust exact test for contingency tables, conditional robust Fisher test, Fisher mid-p test | robust chi-squared test, Cressie-Read power divergence test, adjusted chi-square test, robust contingency test |
| 相关 | 3 | 3 |
| 摘要≠ | The robust Fisher's exact test extends Fisher's classic exact test for contingency tables by applying conservative-correcting adjustments — most commonly the mid-p correction — to reduce the extreme conservatism of the standard exact test. This produces better-calibrated Type I error rates while maintaining validity in small and sparse samples. | The robust chi-square test extends the classic Pearson chi-square framework to remain reliable when standard assumptions — especially the minimum expected-cell-count rule — are violated. Using power divergence statistics (Cressie & Read, 1984) or resampling-based corrections, it produces valid inferences for sparse contingency tables, small samples, and unbalanced categorical data where the ordinary chi-square approximation breaks down. |
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