Bayesian Quantile Regression For Censored Data. Quantile regression permits covariates to affect survival differently at different stages in the followup period thus providing a comprehensive study of the survival distribution. Sep 04 2017 In this work we propose a Bayesian quantile regression method to response variables with mixed discrete-continuous distribution with a point mass at zero where these observations are believed to be left censored or true zeros.
We employ a likelihood-based approach using the asymmetric Laplace error distribution and introduce lagged observed responses into the conditional quantile function. In a Bayesian setting modeling quantiles simultaneously amounts to specifying a survival distribution with the desired quantiles. The asymmetric Laplace likelihood has a special place in the Bayesian quantile regression framework because the usual quantile regression es-.
Jun 09 2011 This paper develops a Bayesian approach to analyzing quantile regression models for censored dynamic panel data.
Simulation study for censored survival data. However inference for these models is challenging particularly for clustered or censored data. Censored regression models are a class of models in which the dependent variable is censored above or below a certain threshold. Quantile regression permits covariates to affect survival differently at different stages in the follow-up period thus providing a comprehensive study of the survival distribution.
