Multicollinearity is often ignored in censored regression model. In this respect, Tobit Liu estimation is proposed as an alternative in order to dispel the adverse effects of multicollinearity on t...

Tobit Liu estimation of censored regression model: an application to Mroz data and a Monte Carlo simulation study

In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced.We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.

/pdf/bayesian-lasso-tobit-regression-4806tfkkt4.pdf

Bayesian Lasso Tobit regression

This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia and explore the correlation between accident severity levels and heterogeneity attributed to unobserved factors. The data from 460 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. By comparing the Bayesian bivariate Tobit quantile and mean regression models, the proposed quantile models showed priority to mean model. Results revealed that low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death, or injury; average speed limit exhibited a close relation with accident rate; and the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injury.

/pdf/investigation-into-interactions-between-accident-2fi138ktwr.pdf

Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach

https://google.iopscience.iop.org/article/10.1088/1742-6596/1294/3/032036/pdf

Bayesian Adaptive Lasso binary regression with ridge parameter

Composite quantile regression methods have been shown to be effective techniques in improving the prediction accuracy. In this article, we propose a Bayesian weighted composite quantile regression estimation procedure to estimate unknown regression coefficients and autoregressive parameters in the linear regression models with autoregressive errors. A Bayesian joint hierarchical model is established using the working likelihood of the asymmetric Laplace distribution. Adaptive Lasso-penalized type priors are used on regression coefficients and autoregressive parameters of the model to conduct inference and variable selection simultaneously. A Gibbs sampling algorithm is developed to simulate the parameters from the posterior distributions. The proposed method is illustrated by some simulation studies and analyzing a real data set. Both simulation studies and real data analysis indicate that the proposed approach performs well. 

Bayesian weighted composite quantile regression estimation for linear regression models with autoregressive errors

Composite quantile regression models have been shown to be effective techniques in improving the prediction accuracy [H. Zou and M. Yuan, Composite quantile regression and the oracle model selectio...

Bayesian composite Tobit quantile regression

In this paper, we propose a new Bayesian lasso inference scheme for variable selection in composite quantile regression model (C Quantile Reg). The suggested approach is to construct a hierarchical structure within the Gibbs sampling under the assumption that the residual term comes from skew Laplace distribution (asymmetric Laplace distribution) and  assign scale mixture uniform (SMU) as prior distributions on the coefficients of composite quantile regression model. Our proposed method was compared to some other existing methods by testing the performance of these methods through simulation studies and real data examples.

https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/download/2734/1074

Fadel Hamid Hadi Alhusseini

Papers

Bayesian composite Tobit quantile regression

New Bayesian Lasso Composite Quantile Regression