6533b7d4fe1ef96bd126343e
RESEARCH PRODUCT
A penalized approach to covariate selection through quantile regression coefficient models
Gianluca SottileMarcello ChiodiPaolo FrumentoMatteo Bottaisubject
Statistics and Probability05 social sciencesQuantile regression model01 natural sciencesQuantile regressionInspiratory capacity010104 statistics & probabilitypenalized quantile regression coefficients modelling (QRCM p )Lasso penalty0502 economics and businessCovariateStatisticsPenalized integrated loss minimization (PILM)tuning parameter selection0101 mathematicsStatistics Probability and UncertaintySelection (genetic algorithm)050205 econometrics MathematicsQuantiledescription
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 selection is quantile-specific, our approach permits using information on all quantiles simultaneously. We describe the estimator, provide simulation results and analyse the data that motivated the present article. The proposed approach is implemented in the qrcmNP package in R.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-03-06 |