Bayesian quantile regression for longitudinal studies with nonignorable missing data.

@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}
}
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 within-subject correlation by introducing a l(2) penalty in the usual QR check function to shrink the subject-specific intercepts and slopes… CONTINUE READING

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