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http://bura.brunel.ac.uk/handle/2438/11937
Title: | Bayesian Tobit quantile regression using-prior distribution with ridge parameter |
Authors: | Alhamzawi, R Yu, K |
Keywords: | G-prior;Gibbs sampler;Ridge parameter;Tobit quantile regression;Variable selection |
Issue Date: | 2015 |
Publisher: | Taylor & Francis |
Citation: | Journal of Statistical Computation and Simulation, 85 (14): pp. 2903 - 2918, (2014) |
Abstract: | A Bayesian approach is proposed for coefficient estimation in the Tobit quantile regression model. The proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression coefficients. The prior is generalized by introducing a ridge parameter to address important challenges that may arise with censored data, such as multicollinearity and overfitting problems. Then, a stochastic search variable selection approach is proposed for Tobit quantile regression model based on g-prior. An expression for the hyperparameter g is proposed to calibrate the modified g-prior with a ridge parameter to the corresponding g-prior. Some possible extensions of the proposed approach are discussed, including the continuous and binary responses in quantile regression. The methods are illustrated using several simulation studies and a microarray study. The simulation studies and the microarray study indicate that the proposed approach performs well. |
URI: | http://www.tandfonline.com/doi/full/10.1080/00949655.2014.945449 http://bura.brunel.ac.uk/handle/2438/11937 |
DOI: | http://dx.doi.org/10.1080/00949655.2014.945449 |
ISSN: | 0094-9655 1563-5163 |
Appears in Collections: | Dept of Mathematics Research Papers |
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