Generalized linear models : a Bayesian perspective / edited by Dipak K. Dey, Sujit K. Ghosh, Bani K. Mallick.
Saved in:
| Online Access: |
Full Text (via Taylor & Francis) |
|---|---|
| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
New York :
Marcel Dekker,
©2000.
|
| Series: | Biostatistics (New York, N.Y.) ;
5. |
| Subjects: |
Table of Contents:
- I. General overview
- 1. Generalized linear models: A Bayesian view
- 2. Random effects in generalized linear mixed models (GLMMs)
- 3. Prior elicitation and variable selection for generalized linear mixed models
- II. Extending the GLMs
- 4. Dynamic generalized linear models
- 5. Bayesian approaches for overdispersion in generalized linear models
- 6. Bayesian generalized linear models for inference about small areas
- III. Categorical and longitudinal data
- 7. Bayesian methods for correlated binary data
- 8. Bayesian analysis for correlated ordinal data models
- 9. Bayesian methods for time series count data
- 10. Item response modeling
- 11. Developing and applying medical practice guidelines following acute myocardial infarction: A case study using Bayesian probit and logit models
- IV. Semiparametric approaches
- 12. Semiparametric generalized linear models: Bayesian approaches
- 13. Binary response regression with normal scale mixture links
- 14. Binary regression using data adaptive robust link functions
- 15. A mixture-model approach to the analysis of survival data
- V. Model diagnostics and variable selection in GLMs
- 16. Bayesian variable selection using the Gibbs sampler
- 17. Bayesian methods for variable selection in the Cox model
- 18. Bayesian model diagnostics for correlated binary data
- VI. Challenging approaches in GLMs
- 19. Bayesian errors-in-variables modeling
- 20. Bayesian analysis of compositional data
- 21. Classification trees
- 22. Modeling and inference for point-referenced binary spatial data
- 23. Bayesian graphical models and software for GLMs.