The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Bayesian Latent Variable Models for Clustered Mixed Outcomes
Abstract
Summary: A general framework is proposed for modelling clustered mixed outcomes. A mixture of generalized linear models is used to describe the joint distribution of a set of underlying variables, and an arbitrary function relates the underlying variables to be observed outcomes. The model accommodates multilevel data structures, general covariate effects and distinct link functions and error distributions for each underlying variable. Within the framework proposed, novel models are developed for clustered multiple binary, unordered categorical and joint discrete and continuous outcomes. A Markov chain Monte Carlo sampling algorithm is described for estimating the posterior distributions of the parameters and latent variables. Because of the flexibility of the modelling framework and estimation procedure, extensions to ordered categorical outcomes and more complex data structures are straightforward. The methods are illustrated by using data from a reproductive toxicity study.
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Cites
Monte Carlo sampling methods using Markov chains and their applications.
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Cited by
Lee, S.-Y., & Song, X.-Y. (2004) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3904_4 · Cited by 488
Extending the rank likelihood for semiparametric copula estimation
D. Hoff, P. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas107 · Cited by 297
A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data
Proust, C., Jacqmin‐Gadda, H., Taylor, J. M. G., Ganiayre, J., & Commenges, D. (2006) · Biometrics · doi:10.1111/j.1541-0420.2006.00573.x · Cited by 107
Vickerstaff, V., Ambler, G., & Omar, R. Z. (2021) · Biometrical Journal · doi:10.1002/bimj.201900040 · Cited by 26
Metadata
- Authors
- Dunson, D. B.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-07-01
- Volume
- 62
- Issue
- 2
- Pages
- 355-366
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2089773781
- Cited by
- 305
- References
- 24
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
