- Bayesian Anal.
- Volume 6, Number 2 (2011), 259-278.
Sensitivity analysis in Bayesian generalized linear mixed models for binary data
Generalized linear mixed models (GLMMs) enjoy increasing popularity because of their ability to model correlated observations. Integrated nested Laplace approximations (INLAs) provide a fast implementation of the Bayesian approach to GLMMs. However, sensitivity to prior assumptions on the random effects precision parameters is a potential problem. To quantify the sensitivity to prior assumptions, we develop a general sensitivity measure based on the Hellinger distance to assess sensitivity of the posterior distributions with respect to changes in the prior distributions for the precision parameters. In addition, for model selection we suggest several cross-validatory techniques for Bayesian GLMMs with a dichotomous outcome. Although the proposed methodology holds in greater generality, we make use of the developed methods in the particular context of the well-known salamander mating data. We arrive at various new findings with respect to the best fitting model and the sensitivity of the estimates of the model components.
Bayesian Anal., Volume 6, Number 2 (2011), 259-278.
First available in Project Euclid: 13 June 2012
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62F15: Bayesian inference
Secondary: 62J12: Generalized linear models 62P10: Applications to biology and medical sciences
Roos, Małgorzata; Held, Leonhard. Sensitivity analysis in Bayesian generalized linear mixed models for binary data. Bayesian Anal. 6 (2011), no. 2, 259--278. doi:10.1214/11-BA609. https://projecteuclid.org/euclid.ba/1339612046