𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Bayesian ordinal and binary regression models with a parametric family of mixture links

✍ Scribed by Joseph B. Lang

Book ID
104306918
Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
666 KB
Volume
31
Category
Article
ISSN
0167-9473

No coin nor oath required. For personal study only.

✦ Synopsis

An ordinal and binary regression model with parametric link is introduced. The link is a member of a one-parameter family of "mixture links", a family that comprises smooth mixtures of the extreme minimum-value, extreme maximum-value, and logistic distributions. A Bayesian version of this exible model serves as a vehicle for introducing a priori information regarding the choice of link. Owing to non-conjugacy, posterior and predictive distributions are approximated using Markov chain Monte Carlo simulation methods. Link-independent, Bayesian interpretations of covariate e ects are described. The method is illustrated through the analyses of several data sets.

πŸ“œ SIMILAR VOLUMES

A BAYESIAN ANALYSIS OF REGRESSION MODELS
A BAYESIAN ANALYSIS OF REGRESSION MODELS WITH CONTINUOUS ERRORS WITH APPLICATION TO LONGITUDINAL STUDIES
✍ LYLE D. BROEMELING; PEYTON COOK πŸ“‚ Article πŸ“… 1997 πŸ› John Wiley and Sons 🌐 English βš– 404 KB πŸ‘ 2 views

We employ a regression model with errors that follow a continuous autoregressive process to analyse longitudinal studies. In this way, unequally spaced observations do not present a problem in the analysis. We employ a Bayesian approach, where our inferences are based on a direct resampling process

NON-PARAMETRIC BAYESIAN APPROACH TO HAZA
NON-PARAMETRIC BAYESIAN APPROACH TO HAZARD REGRESSION: A CASE STUDY WITH A LARGE NUMBER OF MISSING COVARIATE VALUES
✍ ELJA ARJAS; LIPING LIU πŸ“‚ Article πŸ“… 1996 πŸ› John Wiley and Sons 🌐 English βš– 859 KB

A 'packaged' non-parametric multiplicative hazard regression model is proposed, and applied to a study of the effects of some genetic and viral factors in the development of spontaneous leukaemia in mice. Hierarchical modelling and data augmentation are used to deal with the large number of missing