Prior Elicitation in Bayesian Quantile Regression for Longitudinal DataRahim Alhamzawi1*, Keming Yu1 and Jianxin Pan2
- *Corresponding Author:
- Rahim Alhamzawi
Department of Mathematical Sciences
Brunel University, Uxbridge
UB8 3PH, UK
E-mail: [email protected]
Received date: December 02, 2010; Accepted date: April 18, 2011; Published September 25, 2011
Citation: Alhamzawi R, Yu K, Pan J (2011) Prior Elicitation in Bayesian Quantile Regression for Longitudinal Data. J Biomet Biostat 2:115. doi:10.4172/2155-6180.1000115
Copyright: © 2011 Alhamzawi R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In this paper, we introduce Bayesian quantile regression for longitudinal data in terms of informative priors and Gibbs sampling. We develop methods for eliciting prior distribution to incorporate historical data gathered from similar previous studies. The methods can be used either with no prior data or with complete prior data. The advantage of the methods is that the prior distribution is changing automatically when we change the quantile. We propose Gibbs sampling methods which are computationally efficient and easy to implement. The methods are illustrated with both simulation and real data.