<div class="csl-bib-body">
<div class="csl-entry">Ekvall, K. O., & Jones, G. L. (2021). Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions. <i>Electronic Journal of Statistics</i>, <i>15</i>(1). https://doi.org/10.1214/21-ejs1800</div>
</div>
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dc.identifier.issn
1935-7524
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/137292
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dc.description.abstract
We study the convergence properties of a collapsed Gibbs sampler for Bayesian vector autoregressions with predictors, or exogenous variables. The Markov chain generated by our algorithm is shown to be geometrically ergodic regardless of whether the number of observations in the underlying vector autoregression is small or large in comparison to the order and dimension of it. In a convergence complexity analysis, we also give conditions for when the geometric ergodicity is asymptotically stable as the number of observations tends to infinity. Specifically, the geometric convergence rate is shown to be bounded away from unity asymptotically, either almost surely or with probability tending to one, depending on what is assumed about the data generating process. This result is one of the first of its kind for practically relevant Markov chain Monte Carlo algorithms. Our convergence results hold under close to arbitrary model misspecification.
en
dc.language.iso
en
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dc.publisher
INST MATHEMATICAL STATISTICS-IMS
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dc.relation.ispartof
Electronic Journal of Statistics
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dc.subject
Statistics and Probability
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dc.subject
Statistics, Probability and Uncertainty
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dc.title
Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions
en
dc.type
Artikel
de
dc.type
Article
en
dc.type.category
Original Research Article
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tuw.container.volume
15
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1
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true
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true
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A3
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C1
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außerhalb der gesamtuniversitären Forschungsschwerpunkte
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Fundamental Mathematics Research
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tuw.researchTopic.name
Computational Materials Science
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tuw.researchTopic.value
40
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tuw.researchTopic.value
30
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tuw.researchTopic.value
30
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dcterms.isPartOf.title
Electronic Journal of Statistics
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tuw.publication.orgunit
E105-08 - Forschungsbereich Angewandte Statistik
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tuw.publisher.doi
10.1214/21-ejs1800
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dc.identifier.eissn
1935-7524
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dc.description.numberOfPages
30
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wb.sci
true
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1010
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wb.facultyfocus
Wirtschaftsmathematik und Stochastik
de
wb.facultyfocus
Mathematical Methods in Economics and Stochastics
en
wb.facultyfocus.faculty
E100
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Publications
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research article
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item.languageiso639-1
en
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no Fulltext
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restricted
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http://purl.org/coar/resource_type/c_2df8fbb1
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E105-08 - Forschungsbereich Angewandte Statistik
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crisitem.author.parentorg
E105 - Institut für Stochastik und Wirtschaftsmathematik