Bayesian Latent Class Analysis with Shrinkage Priors: An Application to the Hungarian Heart Disease Data

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

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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.
Original languageEnglish
Title of host publicationASMOD 2018 -- Proceedings of the International Conference on Advances in Statistical Modelling of Ordinal Data
Editors Stefania Capecchi and Francesca Di Iorio and Rosaria Simone
Place of PublicationFedOA -- Federico II University Press
Pages13 - 24
Publication statusPublished - 2018

Austrian Classification of Fields of Science and Technology (ÖFOS)

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