Robust gene–environment interaction analysis using penalized trimmed regression

DOI10.1080/00949655.2018.1523411OpenAlexW2890642357WikidataQ91341693 ScholiaQ91341693MaRDI QIDQ4960775

Mengyun Wu, Shuangge Ma, Yaqing Xu, S. Ejaz Ahmed

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1523411

zbMATH Keywords

robustnesspenalized selectionG-E interactiontrimmed regression

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Model averaging for semiparametric varying coefficient quantile regression models

Uses Software

• hierNet

Cites Work

• Nearly unbiased variable selection under minimax concave penalty
• A lasso for hierarchical interactions
• Sparse least trimmed squares regression for analyzing high-dimensional large data sets
• Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection
• Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates
• Robust Linear Model Selection Based on Least Angle Regression
• Post selection shrinkage estimation for high‐dimensional data analysis
• Stability Selection
• Semivarying coefficient least-squares support vector regression for analyzing high-dimensional gene-environmental data
• Variable Selection With the Strong Heredity Constraint and Its Oracle Property
• A Note on High-Dimensional Linear Regression With Interactions

This page was built for publication: Robust gene–environment interaction analysis using penalized trimmed regression