Abstract
The author considers a reparameterized version of the Bayesian ordinal cumulative link regression model as a tool for exploring relationships between covariates and "cutpoint" parameters. The use of this parameterization allows one to fit models using the leapfrog hybrid Monte Carlo method, and to bypass latent variable data augmentation and the slow convergence of the cutpoints which it usually entails. The proposed Gibbs sampler is not model specific and can be easily modified to handle different link functions. The approach is illustrated by considering data from a pediatric radiology study.
Original language | English (US) |
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Pages (from-to) | 715-730 |
Number of pages | 16 |
Journal | Canadian Journal of Statistics |
Volume | 28 |
Issue number | 4 |
DOIs | |
State | Published - Dec 2000 |
Externally published | Yes |
Keywords
- Bayesian hierarchical model
- Hybrid Monte Carlo
- Leapfrog algorithm
- Ordinal regression
- Random walk Metropolis-Hastings
- Staging analysis
ASJC Scopus subject areas
- Statistics and Probability