Feature selection in finite mixture of sparse normal linear models in high-dimensional feature space

DOI10.1093/biostatistics/kxq048zbMath1437.62512OpenAlexW2153510570WikidataQ46129895 ScholiaQ46129895MaRDI QIDQ3303656

Jiahua Chen, Abbas Khalili, Shili Lin

Publication date: 4 August 2020

Published in: Biostatistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biostatistics/kxq048

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

Variable selection using the EM and CEM algorithms in mixtures of linear mixed models ⋮ Robust variable selection for finite mixture regression models

Cites Work

• A unified approach to model selection and sparse recovery using regularized least squares
• High-dimensional variable selection
• Boosting for high-dimensional linear models
• Extended Bayesian information criteria for model selection with large model spaces
• Variable Selection in Finite Mixture of Regression Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
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