Testing the homogeneity of proportions for clustered binary data without knowing the correlation structure

Tsung Shan Tsou, Hsiao Yun Liu

Research output: Contribution to journalArticlepeer-review

Abstract

A robust generalized score test for comparing groups of cluster binary data is proposed. This novel test is asymptotically valid for practically any underlying correlation configurations including the situation when correlation coefficients vary within or between clusters. This structure generally undermines the validity of the typical large sample properties of the method of maximum likelihood. Simulations and real data analysis are used to demonstrate the merit of this parametric robust method. Results show that our test is superior to two recently proposed test statistics advocated by other researchers.

Original languageEnglish
Pages (from-to)1706-1715
Number of pages10
JournalJournal of Applied Statistics
Volume42
Issue number8
DOIs
StatePublished - 3 Aug 2015

Keywords

  • Bartlett's second identity
  • binomial model
  • cluster binary data
  • model misspecification
  • score statistic

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