Comparison from Poisson and Bayesian quantile regression models shows that the effects of vehicle year decrease as the quantile increases, suggesting that the rating factor has lower risk for higher claim counts
Quantile regression models the conditional quantiles of an outcome as a linear combination of predictors. Traditional quantile regression relies on a specific set of loss functions and linear programming for estimation. To introduce Bayesian quantile regression, Yu and Moyeed (2001) use an equivalent ...
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 ...
Bayesian Tobit quantile regression with single-index models[J] . Kaifeng Zhao,Heng Lian.Journal of Statistical Computation and Simulation . 2015 (6)Zhao, K. and Lian, H. (2013). Bayesian Tobit quantile regression with single-index models. Journal of Statistical Computation and Simulation to ...
The Bayesian posterior quantile regression models in each case are studied via Markov chain Monte Carlo (MCMC) sampling strategies. In the nonparametric quantile regression framework, that we contrast to the parametric Bayesian models, we adopted an AL distribution as a proxy and together with the ...
-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....
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...
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 ...
Tobit modelsZero-inflationQuantile regression is a powerful tool for modeling non-Gaussian data, and also for modeling different quantiles of the probability distributions of the responses. We propose a Bayesian approach of estimating the quantiles of multivariate longitudinal data where the responses ...
For more on the frequentist approach to MLR analysis, see Time Series Regression I: Linear Models or [6], Ch. 3. Most tools in Econometrics Toolbox™ are frequentist. A Bayesian approach to estimation and inference of MLR models treats β and σ2 as random variables rather than fixed, ...