Phase randomisation: numerical study of higher cumulants behaviour
For the purpose of testing for stationarity in a time series, a phase randomisation procedure is reviewed and modified, and applied to a wide range of time-series models. These include linear stationary, linear non-stationary, non-linear stationary and non-linear non-stationary processes. Surrogate series are simulated using Standard and Rescaling methods. For all processes, the higher-order central moments of the original series are preserved in the surrogate series using the Rescaling method whereas under the Standard approach only the even central moments are preserved. The density of higher order cumulant estimates obtained under the Rescaling method exhibits unimodality when the process is stationary and multimodality otherwise. The primary aim is to develop a suite of diagnostic tests in order to assess the convergence of Markov Chain Monte Carlo algorithms. Applications of the method as a convergence diagnostic test of Markov Chain Monte Carlo are also discussed.
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|Item Type:||Journal Article|
|Keywords:||Higher cumulants, Markov Chain Monte Carlo, Non, linear time series, Stationarity, Surrogate series|
|Divisions:||Current > QUT Faculties and Divisions > QUT Business School
Past > QUT Faculties & Divisions > Faculty of Science and Technology
|Copyright Owner:||Copyright 2001 Elsevier|
|Copyright Statement:||Reproduced in accordance with the copyright policy of the publisher.|
|Deposited On:||26 Sep 2007 00:00|
|Last Modified:||10 Aug 2011 16:34|
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