Aggregating dependent signals with heavy-tailed combination tests
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View abstract on PubMed
Summary
This summary is machine-generated.Combining p-values from multiple statistical tests is challenging. New methods using Cauchy and harmonic means show promise for dependent p-values, offering power gains over traditional tests in certain scenarios.
Area Of Science
- Statistical inference
- Dependence modeling
- Hypothesis testing
Background
- Combining dependent p-values is a significant challenge in statistical inference.
- Methods like Cauchy and harmonic mean p-value combination are gaining attention for their robustness to unknown dependence.
- Evaluating these methods under asymptotic regimes is crucial for understanding their behavior.
Purpose Of The Study
- To theoretically and empirically evaluate Cauchy and harmonic mean p-value combination tests.
- To investigate the performance of these tests under different types of p-value dependence.
- To compare their validity and power against the Bonferroni test as significance levels approach zero.
Main Methods
- Asymptotic analysis of p-value combination tests.
- Examination of pairwise asymptotically independent and quasi-asymptotically dependent p-values.
- Monte Carlo simulations to assess test validity and power.
- Analysis of test performance based on distribution support and tail heaviness.
Main Results
- Under pairwise asymptotic independence, combination tests are asymptotically valid but converge to the Bonferroni test as significance levels decrease.
- Under pairwise quasi-asymptotic dependence, simulations indicate these tests remain valid and offer power advantages over the Bonferroni test.
- Test performance is influenced by the support and tail heaviness of the underlying distributions.
Conclusions
- Cauchy and harmonic mean p-value combination tests show potential, especially when p-values exhibit substantial dependence.
- These methods can outperform the Bonferroni test in specific dependent scenarios.
- Further investigation into distribution properties is warranted for optimal test selection.
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