On f(R) gravity

A review of the new of the problem of dark energy using modified gravity approach is considered. An explanation of the difficulties facing modern cosmology is given and different approaches are presented. We show why some models of gravity may suffer of instabilities and how some are inconsistent with observations.Comment: 5 pages, 0 figuers. arXiv admin note: text overlap with arXiv:1401.0046 by other author

Bayesian group Lasso regression for left-censored data

In this paper, a new approach for model selection in left-censored regression has been presented. Specifically, we proposed a new Bayesian group Lasso for variable selection and coefficient estimation in left-censored data (BGLRLC). A new hierarchical Bayesian modeling for group Lasso has introduced, which motivate us to propose a new Gibbs sampler for sampling the parameters from the posteriors. The performance of the proposed approach is examined through simulation studies and a real data analysis. Results show that the proposed approach performs well in comparison to other existing methods

Inference with gamma and inverse gamma prior densities in left-censored regression

Left-censored linear regression models are quite popular models and have been deeply considered in the last three decades. In this paper, we consider a completely Bayesian approach for making a new Markov chain Monte Carlo (MCMC) algorithm with tractable full posteriors. Simulated consequences and real data analyses depict that the new Markov chain Monte Carlo algorithm has excellent mixing property and carry out very well than the present methods based on prediction accuracy

Bayesian Tobit quantile regression using-prior distribution with ridge parameter

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

Bayesian Lasso-mixed quantile regression

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