ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2107.06040
431
19
v1v2v3v4 (latest)

The Cauchy Combination Test under Arbitrary Dependence Structures

13 July 2021
M. Long
Zhengbang Li
Wei Zhang
Qizhai Li
ArXiv (abs)PDFHTML
Abstract

Aggregating multiple effects is often encountered in large-scale data analysis where the fraction of significant effects is generally small. Many existing methods cannot handle it effectively because of lack of computational accuracy for small p-values. The Cauchy combination test (abbreviated as CCT) ( J Am Statist Assoc, 2020, 115(529):393-402) is a powerful and computational effective test to aggregate individual ppp-values under arbitrary correlation structures. This work revisits CCT and shows three key contributions including that (i) the tail probability of CCT can be well approximated by a standard Cauchy distribution under much more relaxed conditions placed on individual p-values instead of the original test statistics; (ii) the relaxation conditions are shown to be satisfied for many popular copulas formulating bivariate distributions; (iii) the power of CCT is no less than that of the minimum-type test as the number of tests goes to infinity with some regular conditions. These results further broaden the theories and applications of CCT. The simulation results verify the theoretic results and the performance of CCT is further evaluated with data from a prostate cancer study.

View on arXiv
Comments on this paper