CAFO Working Papers, Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University
Nested Designs with AR Errors via MCMC
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.
Keywords: Bayesian statistics; Metropolis-Hastings algorithm; Markov chain Monte Carlo methods; repeated measurements; autoregressive process; Gibbs sampling; (follow links to similar papers)
JEL-Codes: C11; (follow links to similar papers)
13 pages, October 1, 2007
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