Bayesian Estimation of the Log-linear Exponential Regression Model with Censorship and Collinearity

摘要

In this work, a simulation study is conducted to evaluate the performance of Bayesian estimators for the log-linear exponential regression model under different levels of censoring and degrees of collinearity for two covariates. The diffuse normal, independent Student-t and multivariate Student-t distributions are considered as prior distributions and to draw from the posterior distributions, the Metropolis algorithm is implemented. Also, the results are compared with the maximum likelihood estimators in terms of the mean squared error, coverages and length of the credibility and confidence intervals.