The use of random-effect models for high-dimensional variable selection problems

DOI10.1016/j.csda.2016.05.016zbMath1466.62123OpenAlexW2418020574MaRDI QIDQ1659014

Seungyoung Oh, Youngjo Lee, Sunghoon Kwon

Publication date: 15 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2016.05.016

zbMATH Keywords

variable selectiongeneralized linear modelhigh-dimensionrandom effecthierarchical likelihoodunbounded penalty

Mathematics Subject Classification ID

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

Related Items (5)

On the strong oracle property of concave penalized estimators with infinite penalty derivative at the origin ⋮ Sparse pathway-based prediction models for high-throughput molecular data ⋮ Penalized h‐likelihood approach for variable selection in AFT random‐effect models ⋮ Penalized variable selection in copula survival models for clustered time-to-event data ⋮ Going beyond oracle property: selection consistency and uniqueness of local solution of the generalized linear model

Uses Software

• scca

Cites Work

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