Quantile Regression Models and Their Applications: A ReviewQi Huang1, Hanze Zhang2, Jiaqing Chen3* and Mengying He4
- *Corresponding Author:
- Chen J
College of Science
Wuhan University of Technology
Wuhan, Hubei, 430070, PR China
E-mail: [email protected]
Received date: May 30, 2017; Accepted date: June 16, 2017; Published date: June 20, 2017
Citation: Huang Q, Zhang H, Chen J, He M (2017) Quantile Regression Models and Their Applications: A Review. J Biom Biostat 8: 354. doi: 10.4172/2155-6180.1000354
Copyright: © 2017 Huang Q, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.