A penalized approach to covariate selection through quantile regression coefficient models

Gianluca Sottile, Marcello Chiodi, Matteo Bottai, Bottai Matteo, Gianluca Sottile

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)369-385
Number of pages17
JournalStatistical Modelling
Volume20
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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