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|Title:||Prior elicitation in Bayesian quantile regression for longitudinal data|
|Keywords:||Bayesian quantile regression;Conditional distribution;Gibbs sampling;Longitudinal data;Mixture representation;Random effect|
|Citation:||Journal of Biometrics and Biostatistics, 2: 115, Sep 2011|
|Abstract:||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.|
|Description:||© 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 auhor and source are credited.|
This article has been made available through the Brunel Open Access Publishing Fund.
|Appears in Collections:||Publications|
Brunel OA Publishing Fund
Dept of Mathematics Research Papers
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