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Testing for positive expectation dependence

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  • Xuehu Zhu
  • Xu Guo
  • Lu Lin
  • Lixing Zhu

Abstract

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. Copyright The Institute of Statistical Mathematics, Tokyo 2016

Suggested Citation

  • Xuehu Zhu & Xu Guo & Lu Lin & Lixing Zhu, 2016. "Testing for positive expectation dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 135-153, February.
    • Handle: RePEc:spr:aistmt:v:68:y:2016:i:1:p:135-153
    DOI: 10.1007/s10463-014-0492-7
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    References listed on IDEAS

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