A random subset implementation of weighted quantile sum (WQSRS) regression for analysis of high-dimensional mixtures

DOI10.1080/03610918.2019.1577971zbMath1489.62123OpenAlexW2922449932MaRDI QIDQ5082597

Joshua Kellogg, Paul Curtin, Chris Gennings, Nadja Cech

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1577971

zbMATH Keywords

supervised learningmixturesmixtures modelsweighted quantile sum regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08)

Cites Work

• Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting
• Using random subspace method for prediction and variable importance assessment in linear regression
• Sample and population score matrices and sample correlation matrices from an arbitrary population correlation matrix
• Regularization and Variable Selection Via the Elastic Net

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