Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12504
Title: Bayesian Lasso-mixed quantile regression
Authors: Alhamzawi, R
Yu, K
Keywords: Asymmetric Laplace distribution;Gibbs sampler;Random effects;Longitudinal data;Quantile regression
Issue Date: 2014
Publisher: Taylor & Francis
Citation: Journal of Statistical Computation and Simulation, 84(4), pp. 868 - 880, (2014)
Abstract: In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile regression check function. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of an age-related macular degeneration (ARMD) data, we assess the performance of the proposed method. The simulation studies and the ARMD data analysis indicate that the proposed method performs well in comparison with the other approaches. © 2012 Taylor & Francis.
URI: http://www.tandfonline.com/doi/abs/10.1080/00949655.2012.731689
http://bura.brunel.ac.uk/handle/2438/12504
DOI: http://dx.doi.org/10.1080/00949655.2012.731689
ISSN: 0094-9655
1563-5163
Appears in Collections:Dept of Mathematics Research Papers

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