Department of Mathematics
Testing for positive expectation dependence
© 2014, The Institute of Statistical Mathematics, Tokyo.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.
Expectation dependence, Nonparametric Monte Carlo, Test of Kolmogorov–Smirnov type
Source Publication Title
Annals of the Institute of Statistical Mathematics
Link to Publisher's Edition
Zhu, X., Guo, X., Lin, L., & Zhu, L. (2016). Testing for positive expectation dependence. Annals of the Institute of Statistical Mathematics, 68 (1), 135-153. https://doi.org/10.1007/s10463-014-0492-7