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Gibbs and autoregressive Markov processes

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  • Nieto-Barajas, Luis E.
  • Walker, Stephen G.

Abstract

In this paper we show that particular Gibbs sampler Markov processes can be modified to an autoregressive Markov process. The procedure allows the easy derivation of the innovation variables which provide strictly stationary autoregressive processes with fixed marginals. In particular, we provide the innovation variables for beta, gamma and Dirichlet processes.

Suggested Citation

  • Nieto-Barajas, Luis E. & Walker, Stephen G., 2007. "Gibbs and autoregressive Markov processes," Statistics & Probability Letters, Elsevier, vol. 77(14), pages 1479-1485, August.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:14:p:1479-1485
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    References listed on IDEAS

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    1. Luis E. Nieto‐Barajas & Stephen G. Walker, 2002. "Markov Beta and Gamma Processes for Modelling Hazard Rates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 413-424, September.
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    Cited by:

    1. Bassetti, Federico & Casarin, Roberto & Leisen, Fabrizio, 2014. "Beta-product dependent Pitman–Yor processes for Bayesian inference," Journal of Econometrics, Elsevier, vol. 180(1), pages 49-72.

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