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Nested Designs with AR Errors via MCMC

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    Abstract

    In this paper Markov Chain Monte Carlo algorithms(MCMC) are developed to facilitate the Bayesian analysis on nested designs when the error structure can be expressed as an autoregressive process of order one. Simulated and real data are also presented to confirm the efficiency and high accuracy of our work.

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    Bibliographic Info

    Paper provided by Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University in its series CAFO Working Papers with number 2007:6.

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    Length: 13 pages
    Date of creation: 01 Oct 2007
    Date of revision:
    Handle: RePEc:hhs:vxcafo:2007_006

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    Postal: Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University, SE 351 95 Växjö, Sweden
    Phone: +46 470 70 87 64
    Web page: http://lnu.se/research-groups/cafo?l=en
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    Related research

    Keywords: Bayesian statistics; Metropolis-Hastings algorithm; Markov chain Monte Carlo methods; repeated measurements; autoregressive process; Gibbs sampling;

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