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Bayesian inference for mixed effects models with heterogeneity
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9424-1272
University of New South Wales, Australia.
Uppsala university.
2016 (English)Report (Other academic)
Abstract [en]

We are interested in Bayesian modelling of panel data using a mixed effects model with heterogeneity in the individual random effects. We compare two different approaches for modelling the heterogeneity using a mixture of Gaussians. In the first model, we assume an infinite mixture model with a Dirichlet process prior, which is a non-parametric Bayesian model. In the second model, we assume an over-parametrised finite mixture model with a sparseness prior. Recent work indicates that the second model can be seen as an approximation of the former. In this paper, we investigate this claim and compare the estimates of the posteriors and the mixing obtained by Gibbs sampling in these two models. The results from using both synthetic and real-world data supports the claim that the estimates of the posterior from both models agree even when the data record is finite.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. , 22 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3091
Keyword [en]
Bayesian inference, mixed effects model, panel/longitudinal data, Dirichlet process mixture, finite mixture, sparseness prior
National Category
Probability Theory and Statistics Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-126680ISRN: LiTH-ISY-R-3091Libris ID: 19404891OAI: oai:DiVA.org:liu-126680DiVA: diva2:916319
Projects
CADICS
Funder
Swedish Research Council, 621-2013-5524
Available from: 2016-04-01 Created: 2016-04-01 Last updated: 2016-04-13Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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  • de-DE
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  • en-US
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Output format
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