Efficient and robust optimal design for quantile regression based on linear programming

DOI10.1016/J.CSDA.2023.107892zbMATH Open1543.62168MaRDI QIDQ6554249

Cheng Peng, Stan Uryasev, D. P. Kouri

Publication date: 12 June 2024

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

linear programmingquantile regressionoptimal design of experimentsconditional value-at-risk (CVaR)direct field acoustic testing (DFAT)robust design of experiments

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

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• Random variables, monotone relations, and convex analysis
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• A geometric characterization of \(c\)-optimal designs for heteroscedastic regression
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• Optimal Accelerated Life Designs for Estimation
• Branch and Bound Experiments in Convex Nonlinear Integer Programming
• Optimal Designs for Quantile Regression Models
• Risk-Adapted Optimal Experimental Design
• Model-Robust Designs for Quantile Regression
• Optimum Allocation in Linear Regression Theory
• Optimal subsampling for quantile regression in big data

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