Nested Designs with AR Errors via MCMC

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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Year of publication:	2007-10-01
Authors:	Alkhamisi, Mahdi
Institutions:	Centrum för arbetsmarknadspolitisk forkskning (CAFO), Ekonomihögskolan
Subject:	Bayesian statistics \| Metropolis-Hastings algorithm \| Markov chain Monte Carlo methods \| repeated measurements \| autoregressive process \| Gibbs sampling

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Extent:	application/pdf
Series:	CAFO Working Papers.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series CAFO Working Papers Number 2007:6 13 pages
Classification:	C11 - Bayesian Analysis
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005645257