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|Title:||Bayesian Tobit quantile regression using-prior distribution with ridge parameter|
|Keywords:||G-prior;Gibbs sampler;Ridge parameter;Tobit quantile regression;Variable selection|
|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.|
|Appears in Collections:||Dept of Mathematics Research Papers|
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