Caio L. N. Azevedo
. Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Brazil.
Birnbaum-Saunders Linear Mixed-Effects Models with Censored Data: Bayesian MCMC Inference
Sala 5, Facultad de Matemáticas, Edificio Rolando Chuaqui, Campus San Joaquin, Pontificia Universidad Católica de Chile
It is usual in data analysis the use of linear mixed effects models, when the responses are clustered around some random effects. This paper is focused on the Bayesian inference for the log-Birnabuam-Saunders linear mixed (log-BSLM) models, previously defined in the literature, under a frequentist point of view. The use of Markov chain Monte Carlo (MCMC) method is explored, which provides an alternative to the marginal maximum likelihood approach, which depends on the approximation of the likelihood. We developed, besides parameter estimation, residual analysis, influence diagnostics, model comparison and Bayesian prediction. We developed two MCMC algorithms, with and without consider a certain acceleration procedure. Simulation studies are conducted, under different scenarios of interest, where it is shown that the Bayesian approach, in general, provides better results than the frequentist one. In addition, the algorithm with the acceleration procedure showed to be better, in terms of convergence, than the usual MCMC approach. Also, a real data is analyzed, where is shown that our approach works properly. Finally, some directions toward some extensions are discussed.
Seminario organizado por el Centro para el Descubrimiento de Estructuras en Datos Complejos - MiDaS.