Bayesian quantile regression for longitudinal studies with nonignorable missing data.

Abstract

We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the… (More)
DOI: 10.1111/j.1541-0420.2009.01269.x

Topics

6 Figures and Tables

Cite this paper

@article{Yuan2010BayesianQR, title={Bayesian quantile regression for longitudinal studies with nonignorable missing data.}, author={Ying Yuan and Guosheng Yin}, journal={Biometrics}, year={2010}, volume={66 1}, pages={105-14} }