Geometric Ergodicity of Gibbs and Block Gibbs Samplers for a Hierarchical Random Effects Model
AbstractWe consider fixed scan Gibbs and block Gibbs samplers for a Bayesian hierarchical random effects model with proper conjugate priors. A drift condition given in Meyn and Tweedie (1993, Chapter 15) is used to show that these Markov chains are geometrically ergodic. Showing that a Gibbs sampler is geometrically ergodic is the first step toward establishing central limit theorems, which can be used to approximate the error associated with Monte Carlo estimates of posterior quantities of interest. Thus, our results will be of practical interest to researchers using these Gibbs samplers for Bayesian data analysis.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 67 (1998)
Issue (Month): 2 (November)
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Wilkinson, Darren J & KH Yeung, Stephen, 2004. "A sparse matrix approach to Bayesian computation in large linear models," Computational Statistics & Data Analysis, Elsevier, vol. 44(3), pages 493-516, January.
- Korobilis, Dimitris, 2013.
"Hierarchical shrinkage priors for dynamic regressions with many predictors,"
International Journal of Forecasting,
Elsevier, vol. 29(1), pages 43-59.
- KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," CORE Discussion Papers 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Dimitris Korobilis, 2011. "Hierarchical Shrinkage Priors for Dynamic Regressions with Many Predictors," Working Paper Series 21_11, The Rimini Centre for Economic Analysis.
- Korobilis, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," MPRA Paper 30380, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.