Univariate and multirater ordinal cumulative link regression with covariate specific cutpoints

Canadian Journal of Statistics

Volumn 28, Issue 4, 2000, Pages 715-730

  Ishwaran, Hemant ^a  

^a LERNER RESEARCH INSTITUTE   (United States)

Author keywords

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

Indexed keywords

EID: 0034557372     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: 10.2307/3315912     Document Type: Article

References (21)

1. J. H. Albert & S. Chib (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669-679.
2. J. H. Albert & S. Chib (1998). Bayesian Methods for Cumulative, Sequential and Two-Step Ordinal Data Regression Models. Technical Report.
3. M. K. Cowles (1996). Accelerating Monte Carlo Markov chain convergence for cumulative-link generalized linear models. Statistics and Computing, 6, 101-111.
4. S. Duane & J. B. Kogut (1986). The theory of hybrid stochastic algorithms. Nuclear Physics B, 275, 398-420.
5. S. Duane, A. D. Kennedy, B. J. Pendleton & D. Roweth (1987). Hybrid Monte Carlo. Physics Letters B, 195, 216-222.
6. L. Fahrmeir & G. Tutz (1994). Multivariate Statistical Modelling Based on Generalized Linear Models. Springer-Verlag, New York.
7. C. A. Gatsonis (1995). Random-effects models for diagnostic accuracy data. Academic Radiology, 2 Supplement, S14-S21.
8. P. Gustafson (1997). Large hierarchical Bayesian analysis of multivariate survival data. Biometrics, 53, 230-242.
9. J. A. Hanley & B. J. McNeil (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36.
10. D. Hedeker & R. J. Mermelstein (1998). A multilevel thresholds of change model for analysis of stages of change data. Multivariate Behavioral Research, 33, 427-455.
11. D. Hedeker, R. J. Mermelstein & K. A. Weeks (1999). The thresholds of change model: an approach to analyzing stages of change data. Annals of Behavioral Medicine, 21, 61-70.
12. H. Ishwaran (1999). Applications of hybrid Monte Carlo to Bayesian generalized linear models: quasicomplete separation and neural networks. Journal of Computational and Graphical Statistics, 8, 779-799.
13. H. Ishwaran & C. A. Gatsonis (2000). A general class of hierarchical ordinal regression models with applications to correlated ROC analysis. The Canadian Journal of Statistics, 28, 731-750.
14. V. E. Johnson & J. H. Albert (1998). Ordinal Data Modeling. Springer-Verlag, New York.
15. A. D. Kennedy (1990). The theory of hybrid stochastic algorithms. In Probabilistic methods in quantum field theory and quantum gravity (P. H. Damgaard, H. Huffel & A. Rosenblum, eds.), Plenum Press, New York, pp. 209-223.
16. B. Nandram & M-H. Chen (1996). Reparameterizing the generalized linear model to accelerate Gibbs sampler convergence. Journal of Statistical Computation and Simulation, 54, 129-144.
17. R. M. Neal (1994). An improved acceptance procedure for the hybrid Monte Carlo algorithm. Journal of Computational Physics, 111, 194-203.
18. R. M. Neal (1996). Bayesian Learning for Neural Networks. Springer-Verlag, New York.
19. N. Obuchowski (1995). Multi-Reader multi-modality ROC studies: Hypothesis testing and sample size estimation using an ANOVA approach with dependent observations. Academic Radiology, 2 Supplement, S22-S29.
20. M. L. Thompson & W. Zucchini (1989). On the statistical analysis of ROC curves. Statistics in Medicine, 8, 1277-1290.
21. A. N. Tosteson & C. B. Begg (1988). A general regression methodology for ROC curve estimation. Medical Decision Making, 8, 204-215.