On Gibbs sampler and Metrolopolis-Hastings applied to pairwise poisson and car crash data
(English)Manuscript (preprint) (Other academic)
We address the use of MCMC methods in the presence of incidental parameters. Two different models are studied, one pairwise Poisson model and a model for car crash data. In both models, a Bayesian analysis is made where the posterior density is obtained by MCMC methods. The empirical KL-measure is used to diagnose convergence. Credible intervals for structural parameters are computed in both models and compared to the corresponding confidence intervals obtained by frequentist analysis. Although some differences are observed, the methods give qualitatively the same conclusions.
MCMC, KL-measure, convergence diagnostic, incidental parameters, bootstrap, collision safety
IdentifiersURN: urn:nbn:se:liu:diva-86860OAI: oai:DiVA.org:liu-86860DiVA: diva2:582907