Bayesian quantile regression for ordinal longitudinal data

Bayesian Quantile Regression with Adaptive Elastic Net Penalty for Longitudinal Data

Longitudinal studies include the important parts of epidemiological surveys, clinical trials and social studies. In longitudinal studies, measurement of the responses is conducted repeatedly through time. Often, the main goal is to characterize the change in responses over time and the factors that influence the change. Recently, to analyze this kind of data, quantile regression has been taken ...

Bayesian quantile regression for longitudinal studies with nonignorable missing data.

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 introduc...

Quantile Regression for Longitudinal Data

The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a large number of “fixed effects”. The introduction of a large number of individual fixed effects can significantly inflate the variability of estimates of other covariate effects. Regularization, or shrinkage of these individual effects toward...

Bayesian Quantile Regression with Adaptive Lasso Penalty for Dynamic Panel Data

‎Dynamic panel data models include the important part of medicine‎, ‎social and economic studies‎. ‎Existence of the lagged dependent variable as an explanatory variable is a sensible trait of these models‎. ‎The estimation problem of these models arises from the correlation between the lagged depended variable and the current disturbance‎. ‎Recently‎, ‎quantile regression to analyze dynamic pa...

Logistic Regression for Multivariate Longitudinal Ordinal Data