Efficient information-based criteria for model selection in quantile regression

Wooyoung Shin, Mingang Kim, Yoonsuh Jung

Research output: Contribution to journalArticlepeer-review


Information-based model selection criteria such as the AIC and BIC employ check loss functions to measure the goodness of fit for quantile regression models. Model selection using a check loss function is robust due to its resistance to outlying observations. In the present study, we suggest modifying the check loss function to achieve a more efficient goodness of fit. Because the cusp of the check loss is quadratically adjusted in the modified version, greater efficiency (or variance reduction) in the model selection is expected. Because we focus on model selection here, we do not modify the model-fitting process. Generalized cross-validation is another common method for choosing smoothing parameters in quantile smoothing splines. We describe how this can be adjusted using the modified check loss to increase efficiency. The proposed generalized cross-validation is designed to reflect the target quantile and sample size. Two real data sets and simulation studies are presented to evaluate its performance using linear and nonlinear quantile regression models.

Original languageEnglish
Pages (from-to)245-281
Number of pages37
JournalJournal of the Korean Statistical Society
Issue number1
Publication statusPublished - 2022 Mar

Bibliographical note

Funding Information:
Yoonsuh Jung’s work is partially supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (MIST) (No. 2019R1F1A1040515 and No. 2019R1A4A1028134).

Publisher Copyright:
© 2021, Korean Statistical Society.


  • AIC
  • BIC
  • Check loss
  • GCV
  • Model validation
  • Quantile regression

ASJC Scopus subject areas

  • Statistics and Probability


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