Quantile regression for mixed models with an application to examine blood pressure trends in China
From MaRDI portal
Publication:902897
DOI10.1214/15-AOAS841zbMath1454.62400arXiv1511.01641WikidataQ37567370 ScholiaQ37567370MaRDI QIDQ902897
Penny Gordon-Larsen, Montserrat Fuentes, Brian J. Reich, Luke B. Smith
Publication date: 4 January 2016
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1511.01641
Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10)
Related Items (3)
Bayesian non-crossing quantile regression for regularly varying distributions ⋮ Quantile regression for mixed models with an application to examine blood pressure trends in China ⋮ Robust Bayesian small area estimation based on quantile regression
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Linear quantile mixed models
- Bayesian inference for additive mixed quantile regression models
- Empirical likelihood for quantile regression models with longitudinal data
- Quantile regression for mixed models with an application to examine blood pressure trends in China
- Nonparametric spatial models for extremes: application to extreme temperature data
- Heavy-tailed longitudinal data modeling using copulas
- Inference for censored quantile regression models in longitudinal studies
- An introduction to copulas. Properties and applications
- On multivariate quantile regression
- Quantile regression for longitudinal data
- Noncrossing quantile regression curve estimation
- Quasi-Likelihood for Median Regression Models
- Semiparametric Approach to a Random Effects Quantile Regression Model
- Copula-based nonlinear quantile autoregression
- Regression models for data with a non-zero probability of a zero response
- Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence
- Bayesian survival analysis
This page was built for publication: Quantile regression for mixed models with an application to examine blood pressure trends in China
