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- W4386794913 abstract "This paper introduces a comprehensive framework to adjust a discrete test statistic for improving its hypothesis testing procedure. The adjustment minimizes the Wasserstein distance to a null-approximating continuous distribution, tackling some fundamental challenges inherent in combining statistical significances derived from discrete distributions. The related theory justifies Lancaster's mid-p and mean-value chi-squared statistics for Fisher's combination as special cases. However, in order to counter the conservative nature of Lancaster's testing procedures, we propose an updated null-approximating distribution. It is achieved by further minimizing the Wasserstein distance to the adjusted statistics within a proper distribution family. Specifically, in the context of Fisher's combination, we propose an optimal gamma distribution as a substitute for the traditionally used chi-squared distribution. This new approach yields an asymptotically consistent test that significantly improves type I error control and enhances statistical power." @default.
- W4386794913 created "2023-09-16" @default.
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- W4386794913 date "2023-09-14" @default.
- W4386794913 modified "2023-10-01" @default.
- W4386794913 title "A minimum Wasserstein distance approach to Fisher's combination of independent discrete p-values" @default.
- W4386794913 doi "https://doi.org/10.48550/arxiv.2309.07692" @default.
- W4386794913 hasPublicationYear "2023" @default.
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