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

Grün, Bettina ORCID: https://orcid.org/0000-0001-7265-4773 and Malsiner-Walli, Gertraud (2018) Bayesian Latent Class Analysis with Shrinkage Priors: An Application to the Hungarian Heart Disease Data. In: ASMOD 2018 -- Proceedings of the International Conference on Advances in Statistical Modelling for Ordinal Data. FedOA -- Federico II University Press, Napoli. pp. 13-24. ISBN 978-88-6887-042-3

Preview

PDF heart.pdf Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0). Download (3MB)

Abstract

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.

Item Type:	Book Section
Keywords:	Bayesian latent class analysis, Shrinkage prior, Variable selection
Divisions:	Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document:	Published
Depositing User:	Gertraud Novotny
Date Deposited:	29 Oct 2018 13:13
Last Modified:	17 Aug 2021 10:40
Related URLs:	http://www.fedoabooks.unina.it
FIDES Link:	https://bach.wu.ac.at/d/research/results/87432/
URI:	https://epub.wu.ac.at/id/eprint/6612

Actions

View Item

Downloads