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MCMC for integer valued ARMA processes

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>01/2007
<mark>Journal</mark>Journal of Time Series Analysis
Issue number1
Number of pages19
Pages (from-to)92-110
Publication StatusPublished
<mark>Original language</mark>English


The classical statistical inference for integer-valued time-series has primarily been restricted to the integer-valued autoregressive (INAR) process. Markov chain Monte Carlo (MCMC) methods have been shown to be a useful tool in many branches of statistics and is particularly well suited to integer-valued time-series where statistical inference is greatly assisted by data augmentation. Thus in this article, we outline an efficient MCMC algorithm for a wide class of integer-valued autoregressive moving-average (INARMA) processes. Furthermore, we consider noise corrupted integer-valued processes and also models with change points. Finally, in order to assess the MCMC algorithms inferential and predictive capabilities we use a range of simulated and real data sets.