The use of random-effect models for high-dimensional variable selection problems
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Publication:1659014
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
variable selectiongeneralized linear modelhigh-dimensionrandom effecthierarchical likelihoodunbounded penalty
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
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