Latent class analysis explains dependency structures in multivariate categorical
data by assuming the presence of latent classes. We investigate the specification of suitable
priors for the Bayesian latent class model to determine the number of classes and perform
variable selection. Estimation is possible using standard tools implementing general purpose
Markov chain Monte Carlo sampling techniques such as the software JAGS. However, class
specific inference requires suitable post-processing in order to eliminate label switching. The
proposed Bayesian specification and analysis method is applied to the Hungarian heart disease
data set to determine the number of classes and identify relevant variables and results are
compared to those obtained with the standard prior for the component specific parameters.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:6612 |
| Date | January 2018 |
| Creators | Grün, Bettina, Malsiner-Walli, Gertraud |
| Publisher | FedOA -- Federico II University Press |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Book Section, PeerReviewed |
| Format | application/pdf |
| Rights | Creative Commons: Attribution 4.0 International (CC BY 4.0) |
| Relation | http://dx.doi.org/10.6093/978-88-6887-042-3, http://www.fedoabooks.unina.it, http://epub.wu.ac.at/6612/ |
Page generated in 0.0027 seconds