Autori
Ouhourane, Mohamed
Yang, Yi
Oualkacha, Karim

Titolo
Group penalized quantile regression
Periodico
Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2022 - Volume: 31 - Fascicolo: 3 - Pagina iniziale: 495 - Pagina finale: 529

Quantile regression models have become a widely used statistical tool in genetics and in the omics fields because they can provide a rich description of the predictors’ effects on an outcome without imposing stringent parametric assumptions on the outcome-predictors relationship. This work considers the problem of selecting grouped variables in high-dimensional linear quantile regression models. We introduce a group penalized pseudo quantile regression (GPQR) framework with both group-lasso and group non-convex penalties. We approximate the quantile regression check function using a pseudo-quantile check function. Then, using the majorization–minimization principle, we derive a simple and computationally efficient group-wise descent algorithm to solve group penalized quantile regression. We establish the convergence rate property of our algorithm with the group-Lasso penalty and illustrate the GPQR approach performance using simulations in high-dimensional settings. Furthermore, we demonstrate the use of the GPQR method in a gene-based association analysis of data from the Alzheimer’s Disease Neuroimaging Initiative study and in an epigenetic analysis of DNA methylation data.



SICI: 1618-2510(2022)31:3<495:GPQR>2.0.ZU;2-9

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