Error-Function-Based Penalized Quantile Regression in the Linear Mixed Model
Source abstract
Abstract
We study a novel Doubly penalized ERror Function regularized Quantile Regression (DERF-QR) in this paper. This is a method of variable selection by ERror Function (ERF) regularization in the linear effects model. We introduce a two-stage iterative algorithm combining the iterative reweighted L1 approach and the alternating direction method of multipliers (ADMM) to estimate the model parameters. Numerical simulations show that by using this method we remove redundant variables effectively and obtain accurate coefficient estimations. Our method outperforms two existing penalized quantile regression methods in various error conditions by comparison. Finally, we apply the methodology in a financial dataset and showcase its practicality.
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Research context
OpenAlex topics: Advanced Statistical Methods and Models · Statistical Methods and Inference · Face and Expression Recognition
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Affiliations
School of Mathematics, Hohai University, Nanjing 210098, China
Funding
Double First Class Special Project, Jiangsu, China · Hohai University
References (25)
Show source-provided references
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