A Note on the Hierarchical Model and Power Prior Distribution in Bayesian Quantile Regression
نویسندگان
چکیده
منابع مشابه
Power Prior Elicitation in Bayesian Quantile Regression
We address a quantile dependent prior for Bayesian quantile regression. We extend the idea of the power prior distribution in Bayesian quantile regression by employing the likelihood function that is based on a location-scale mixture representation of the asymmetric Laplace distribution. The propriety of the power prior is one of the critical issues in Bayesian analysis. Thus, we discuss the pr...
متن کاملBayesian quantile regression using random B-spline series prior
A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ1 and ξ2 not depending on the prediction variables. In a Bayesian approach, a prio...
متن کاملBayesian Tobit quantile regression using g-prior distribution with ridge parameter
A Bayesian approach is proposed for coefficient estimation in 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...
متن کاملObjective Bayesian analysis on the quantile regression
The dissertation consists of two distinct but related research projects. First of all, we study the Bayesian analysis on the two-piece location-scale models, which contain several well-known subdistributions, such as the asymmetric Laplace distribution, the -skew normal distribution, and the skewed Student-t distribution. The use of two-piece location-scale models is an attractive method to mod...
متن کاملBayesian quantile regression
1. Introduction: Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, θ) = 0 where θ is a k dimensional parameter of interest and k may be smaller, equal to or larger than m. Th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Economics and Administrative Sciences
سال: 2014
ISSN: 2227-703X,2518-5764
DOI: 10.33095/jeas.v20i76.690