Univariate and multirater ordinal cumulative link regression with covariate specific cutpoints

Research output: Contribution to journalArticle

21 Scopus citations

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 languageEnglish (US)
Pages (from-to)715-730
Number of pages16
JournalCanadian Journal of Statistics
Volume28
Issue number4
DOIs
StatePublished - Dec 2000
Externally publishedYes

Keywords

  • Bayesian hierarchical model
  • Hybrid Monte Carlo
  • Leapfrog algorithm
  • Ordinal regression
  • Random walk Metropolis-Hastings
  • Staging analysis

ASJC Scopus subject areas

  • Statistics and Probability

Fingerprint Dive into the research topics of 'Univariate and multirater ordinal cumulative link regression with covariate specific cutpoints'. Together they form a unique fingerprint.

  • Cite this