Bayesian inference for bivariate generalized linear models in diagnosing renal arterial obstruction

Cengiz M. A.

Statistical Methodology, cilt.2, sa.3, ss.168-174, 2005 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 2 Sayı: 3
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1016/j.stamet.2005.03.001
  • Dergi Adı: Statistical Methodology
  • Derginin Tarandığı İndeksler: Scopus
  • Sayfa Sayıları: ss.168-174
  • Anahtar Kelimeler: Bayesian analysis, Bivariate generalized linear model, Renal arterial obstruction
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Generalized linear models are well-established generalizations of the linear models used for regression and analysis of variance. They allow flexible mean structures and general distributions, other than the linear link and normal response assumed in regression. Further enhancements using ideas from multivariate analysis improve power and precision by modelling dependencies between response variables. This paper focuses on the specific case of regression models for bivariate Bernoulli responses and investigates their analysis using a Bayesian approach. The important problem of renal arterial obstruction is considered, as a medical application of these models. © 2005 Elsevier B.V. All rights reserved.