0000000000243153
AUTHOR
Matteo Bottai
A penalized approach to covariate selection through quantile regression coefficient models
The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…
Nonlinear parametric quantile models
Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper …
sj-zip-1-smm-10.1177_0962280220941159 - Supplemental material for Nonlinear parametric quantile models
Supplemental material, sj-zip-1-smm-10.1177_0962280220941159 for Nonlinear parametric quantile models by Matteo Bottai and Giovanna Cilluffo in Statistical Methods in Medical Research