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

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  • Kwon, Sunghoon
  • Oh, Seungyoung
  • Lee, Youngjo

Abstract

We study the use of random-effect models for variable selection in high-dimensional generalized linear models where the number of covariates exceeds the sample size. Certain distributional assumptions on the random effects produce a penalty that is non-convex and unbounded at the origin. We introduce a unified algorithm that can be applied to various statistical models including generalized linear models. Simulation studies and data analysis are provided.

Suggested Citation

  • Kwon, Sunghoon & Oh, Seungyoung & Lee, Youngjo, 2016. "The use of random-effect models for high-dimensional variable selection problems," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 401-412.
    • Handle: RePEc:eee:csdana:v:103:y:2016:i:c:p:401-412
    DOI: 10.1016/j.csda.2016.05.016
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    References listed on IDEAS

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    3. Lee Woojoo & Lee Donghwan & Lee Youngjo & Pawitan Yudi, 2011. "Sparse Canonical Covariance Analysis for High-throughput Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-24, July.
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    Cited by:

    1. Olivier Collignon & Jeongseop Han & Hyungmi An & Seungyoung Oh & Youngjo Lee, 2018. "Comparison of the modified unbounded penalty and the LASSO to select predictive genes of response to chemotherapy in breast cancer," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-15, October.
    2. Alexander Kirpich & Elizabeth A Ainsworth & Jessica M Wedow & Jeremy R B Newman & George Michailidis & Lauren M McIntyre, 2018. "Variable selection in omics data: A practical evaluation of small sample sizes," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-19, June.
    3. Lee, Sangin & Lee, Youngjo & Pawitan, Yudi, 2018. "Sparse pathway-based prediction models for high-throughput molecular data," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 125-135.

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