alexa Prior Elicitation in Bayesian Quantile Regression for Longitudinal Data
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Prior Elicitation in Bayesian Quantile Regression for Longitudinal Data

Rahim Alhamzawi1*, Keming Yu1 and Jianxin Pan2

1Department of Mathematical Sciences, Brunel University, Uxbridge, UB8 3PH, UK

2School of Mathematics, University of Manchester, Manchester, M13 9PL, UK

*Corresponding Author:
Rahim Alhamzawi
Department of Mathematical Sciences
Brunel University, Uxbridge
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.


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