Phase randomisation: a convergence diagnostic test for MCMC
Most MCMC users address the convergence problem by applying diagnostic tools to the output produced by running their samplers. Potentially useful diagnostics may be borrowed from diverse areas such as time series. One such method is phase randomisation. The aim of this paper is to describe this method in the context of MCMC, summarise its characteristics, and contrast its performance with those of the more common diagnostic tests for MCMC. It is observed that the new tool contributes information about third and higher order cumulant behaviour which is important in characterising certain forms of nonlinearity and nonstationarity.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Keywords:||Convergence diagnostics, higher cumulants, Markov Chain Monte Carlo, non, linear time series, stationarity, surrogate series|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)|
|Divisions:||Current > QUT Faculties and Divisions > QUT Business School|
|Copyright Owner:||Copyright 2005 Blackwell Publishing|
|Copyright Statement:||The definitive version is available at www.blackwell-synergy.com|
|Deposited On:||05 Jan 2007|
|Last Modified:||29 Feb 2012 23:17|
Repository Staff Only: item control page