Parameter estimation in a hierarchical random intercept model with censored response: An approach using a SEM algorithm and Gibbs sampling

4^th International Conference and Exhibition on Biometrics & Biostatistics

November 16-18, 2015 San Antonio, USA

Yousri Slaoui

University of Poitiers, France

Posters-Accepted Abstracts: J Appl Computat Math

Abstract :

In this talk, 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. As an application, we consider a dataset consisting of 2941 parasite density measurement gathered over a population of 505 Senegal children between 2001 and 2003. Assuming that all these measurements are correct, we simulate the effect of various censure levels by removing the corresponding entries before performing our algorithm. The model residuals are then compared to those obtained with the full data. Even when 10%, 20% or even 30% of the original measurements are missing, the produced residuals remain very accurate thus demontrating the effectiveness of our approach. Moreover, 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.

Biography :

Email: Yousri.Slaoui@math.univ-poitiers.fr