Bayesian quantile regression modelsResponse variable with multiple outputsNoncrossing conditional quantilesStructured additive predictorsIncome and health inequalityBrazilian High School National ExamQuantile regression models are a powerful tool for studying different points of the conditional distribution of ...
-Hastings algorithm.The approach of Chib (1995) for Gibbs output, as extended by Chib and Jeliazkov(2001) for Metropolis-Hastings chains, is also used to estimate the marginal likelihood of our model.This leads to a Bayesian framework for comparing alternative Tobit quantile regression models....
In this paper, we develop a fully Bayesian adaptive Lasso approach for quantile regression models with nonignorably missing response data, where the nonignorable missingness mechanism is specified by a logistic regression model. The proposed method extends the Bayesian Lasso by allowing different ...
In this paper, we incorporate Bayesian bridge-randomized penalty and its adaptive version into the quantile regression (QR) models with autoregressive perturbations to conduct Bayesian penalization estimation. Employing the working likelihood of the asymmetric Laplace distribution (ALD) perturbations, the ...
For the second building block of our method, we propose a dynamic procedure for selecting and combining dynamic quantile regression (QR) models. The challenge of determining which regressors to include in the model can be addressed from two perspectives. One approach is to use a variable dimension...
Bayesian quantile regression framework. Kottas and Gelfand (2001) and Kottas and Krnjaji´c (2009) used Dirichlet process mixture models. Reich, Bondell and Wang (2008) assumed the error distributions to be an infi- nite mixture of normals. Dunson and Taylor (2005) used an approximate ...
Quantile regressionNew Zero-inflated models Sample-selection models Survival models Panel-data models Multilevel models Autoregressive models Threshold autoregressive models (TAR) Multivariate time-series models Multiple-equation models Lasso Econometrics models ...
Bayesian Endogenous Tobit Quantile Regression This study proposes $p$-th Tobit quantile regression models with endogenousvariables. In the first stage regression of the endogenous variable on theexogenous variables, the assumption that the $\\alpha$-th quantile of the errorterm is z... G Kobayashi...
8.Research on Bayesian Analysis of Autoregressive Conditional Heteroscedaticity Models and Their Application;自回归条件异方差模型的贝叶斯分析及其应用研究 9.Bayesian Semiparametric Cox Regression Analysis of Interval-censored Data区间删失数据下比例风险回归模型的贝叶斯半参数分析 10.Measure Market Risk Based on...
Multivariate linear regression Nonlinear model: Change-point analysis Using the GUI to perform Bayesian analysis Multiple chains Gelman–Rubin convergence diagnostic Predictions Programming your own Bayesian models Quantile regressionStataNow Longitudinal/panel-data models ...