Bayesian Inference for Heterogeneous Event Counts
dc.contributor.author | Martin, Andrew D. | |
dc.date.accessioned | 2015-12-21T16:14:58Z | |
dc.date.available | 2015-12-21T16:14:58Z | |
dc.date.issued | 2003-08 | |
dc.identifier.citation | Andrew D. Martin. 2003. “Bayesian Inference for Heterogeneous Event Counts.” Sociological Methods and Research. 32: 30-63. | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/116234 | |
dc.description.abstract | This article presents an integrated set of Bayesian tools one can use to model heterogeneous event counts. While models for event count cross sections are now widely used, little has been written about how to model counts when contextual factors introduce heterogeneity. The author begins with a discussion of Bayesian cross-sectional count models and discusses an alternative model for counts with overdispersion. To illustrate the Bayesian framework, the author fits the model to the number of women’s rights cosponsorships for each member of the 83rd to 102nd House of Representatives. The model is generalized to allow for contextual heterogeneity. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explanations, even with a small number of contextual units. The author compares the estimates from this model with traditional approaches and discusses software one can use to easily implement these Bayesian models with little start-up cost | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Sage Publications, Inc. | en_US |
dc.subject | event count | en_US |
dc.subject | Markov chain Monte Carlo | en_US |
dc.subject | hierarchical Bayes | en_US |
dc.subject | multilevel models | en_US |
dc.subject | BUGS | en_US |
dc.title | Bayesian Inference for Heterogeneous Event Counts | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Political Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | LSA Dean's Office | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/116234/1/smr03.pdf | |
dc.identifier.doi | 10.1177/0049124103253500 | |
dc.identifier.source | Sociological Methods and Research | en_US |
dc.identifier.orcid | 0000-0002-6532-0721 | en_US |
dc.identifier.name-orcid | Martin, Andrew; 0000-0002-6532-0721 | en_US |
dc.owningcollname | Political Science |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.