Bayesian joint quantile regression for mixed effects models with censoring and errors in covariates-Reference-Cited by-同舟云学术

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Bayesian joint quantile regression for mixed effects models with censoring and errors in covariates

Published:2016-05-03 Issue:3 Volume:31 Page:1031-1057
ISSN:0943-4062
Container-title:Computational Statistics
language:en
Short-container-title:Comput Stat

Author:

Tian Yuzhu,Li Er’qian,Tian Maozai

Funder

National Natural Science Foundation of China

Key Scientific Research Project of Henan Province Universities of China

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s00180-016-0659-1.pdf

Reference40 articles.

1. Alhamzawi R, Yu K, Benoit DF (2012) Bayesian adaptive Lasso quantile regression. Stat Model 12:279–297

2. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Measurement error in nonlinear models, 2nd edn. Chapman and Hall, New York

3. Connick E, Lederman MM, Kotzin BL et al (2000) Immune reconstitution in the first year of potent antiretroviral therapy and its relationship to virologic response. J Infect Dis 181:358–363

4. Dagne GA, Huang YX (2012) Mixed-effects Tobit joint models for longitudinal data with skewness, detection limits, and measurement errors. J Probab Stat. doi: 10.1155/2012/614102

5. Davino C, Furno M, Vistocco D (2014) Quantile regression: theory and applications. Wiley, New York

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