Testing for positive expectation dependence

Xuehu Zhu, Xu Guo, Lu Lin and Lixing Zhu

Annals of the Institute of Statistical Mathematics, 2016, vol. 68, issue 1, 135-153

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

Keywords: Expectation dependence; Nonparametric Monte Carlo; Test of Kolmogorov–Smirnov type (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:aistmt:v:68:y:2016:i:1:p:135-153

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DOI: 10.1007/s10463-014-0492-7

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