Bayesian dynamic modeling of latent trait distributions.

Journal Article (Journal Article)

Studies of latent traits often collect data for multiple items measuring different aspects of the trait. For such data, it is common to consider models in which the different items are manifestations of a normal latent variable, which depends on covariates through a linear regression model. This article proposes a flexible Bayesian alternative in which the unknown latent variable density can change dynamically in location and shape across levels of a predictor. Scale mixtures of underlying normals are used in order to model flexibly the measurement errors and allow mixed categorical and continuous scales. A dynamic mixture of Dirichlet processes is used to characterize the latent response distributions. Posterior computation proceeds via a Markov chain Monte Carlo algorithm, with predictive densities used as a basis for inferences and evaluation of model fit. The methods are illustrated using data from a study of DNA damage in response to oxidative stress.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB

Published Date

  • October 2006

Published In

Volume / Issue

  • 7 / 4

Start / End Page

  • 551 - 568

PubMed ID

  • 16488893

Electronic International Standard Serial Number (EISSN)

  • 1468-4357

International Standard Serial Number (ISSN)

  • 1465-4644

Digital Object Identifier (DOI)

  • 10.1093/biostatistics/kxj025


  • eng