Testing for positive expectation dependence

Xuehu Zhu, Xu Guo, Lu Lin, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


In this paper, hypothesis testing for positive first-degree and higher-degree expectation dependence is investigated. Some tests of Kolmogorov–Smirnov type are constructed, which are shown to control type I error well and to be consistent against global alternative hypothesis. Further, the tests can also detect local alternative hypotheses distinct from the null hypothesis at a rate as close to the square root of the sample size as possible, which is the fastest possible rate in hypothesis testing. A nonparametric Monte Carlo test procedure is applied to implement the new tests because both sampling and limiting null distributions are not tractable. Simulation studies and a real data analysis are carried out to illustrate the performances of the new tests.

Original languageEnglish
Pages (from-to)135-153
Number of pages19
JournalAnnals of the Institute of Statistical Mathematics
Issue number1
Publication statusPublished - 1 Feb 2016

Scopus Subject Areas

  • Statistics and Probability

User-Defined Keywords

  • Expectation dependence
  • Nonparametric Monte Carlo
  • Test of Kolmogorov–Smirnov type


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