Sparsity Oriented Importance Learning for High-Dimensional Linear Regression

DOI10.1080/01621459.2017.1377080zbMath1409.62138arXiv1608.00629OpenAlexW2493959018MaRDI QIDQ3121571

Chenglong Ye, Yuhong Yang, Yi Yang

Publication date: 20 March 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.00629

zbMATH Keywords

model averagingvariable importanceadaptive regression by mixingreliability and reproducibility

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items

Performance Assessment of High-dimensional Variable Identification, Consistency of BIC Model Averaging, On improvability of model selection by model averaging, Multifold Cross-Validation Model Averaging for Generalized Additive Partial Linear Models, A comparative study on high-dimensional bayesian regression with binary predictors, Model averaging for semiparametric varying coefficient quantile regression models, The scalable birth-death MCMC algorithm for mixed graphical model learning with application to genomic data integration, Cross-Validation With Confidence, Corrected Mallows criterion for model averaging, Visualization and assessment of model selection uncertainty

Uses Software

• relaimpo
• randomForest

Cites Work

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