Journal article
Bayesian ordinal and binary regression models with a parametric family of mixture links
Computational statistics & data analysis, Vol.31(1), pp.59-87
1999
DOI: 10.1016/S0167-9473(99)00007-9
Abstract
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 flexible 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 effects are described. The method is illustrated through the analyses of several data sets.
Details
- Title: Subtitle
- Bayesian ordinal and binary regression models with a parametric family of mixture links
- Creators
- Joseph B Lang - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.31(1), pp.59-87
- Publisher
- Elsevier B.V
- DOI
- 10.1016/S0167-9473(99)00007-9
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Language
- English
- Date published
- 1999
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984257626102771
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