Parameter Estimation in a Hierarchical Random Intercept Model with Censored Response: An Approach using a SEM Algorithm and Gibbs Sampling

Abstract

In this paper, we propose an approach, based on the stochastic expectation maximization (SEM) algorithm and Gibbs sampling, to deal with the problem caused by censoring in the response of a hierarchical random intercept models. We compared our approach with the existing methods via real data sets as well as simulations. Results showed that our approach outperformed other approaches in terms of estimation accuracy and computing efficiency.