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MCMC for Integer-Valued ARMA processes

  • Peter Neal
  • T. Subba Rao
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    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. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.

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    Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

    Volume (Year): 28 (2007)
    Issue (Month): 1 (01)
    Pages: 92-110

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    Handle: RePEc:bla:jtsera:v:28:y:2007:i:1:p:92-110
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