Permutation principles for the change analysis of stochastic processes under strong invariance
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摘要
Approximations of the critical values for change-point tests are obtained through permutation methods. Both, abrupt and gradual changes are studied in models of possibly dependent observations satisfying a strong invariance principle, as well as gradual changes in an i.i.d. model. The theoretical results show that the original test statistics and their corresponding permutation counterparts follow the same distributional asymptotics. Some simulation studies illustrate that the permutation tests behave better than the original tests if performance is measured by the α- and β-error, respectively.
论文关键词:Permutation principle,Bootstrap,Change-point,Invariance principle,Abrupt change,Gradual change,Rank statistic,Limiting extreme value distribution
论文评审过程:Received 19 September 2004, Revised 30 January 2005, Available online 17 May 2005.
论文官网地址:https://doi.org/10.1016/j.cam.2005.03.065