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A new sparse variable selection via random-effect model

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  • Lee, Youngjo
  • Oh, Hee-Seok

Abstract

We study a new approach to simultaneous variable selection and estimation via random-effect models. Introducing random effects as the solution of a regularization problem is a flexible paradigm and accommodates likelihood interpretation for variable selection. This approach leads to a new type of penalty, unbounded at the origin and provides an oracle estimator without requiring a stringent condition. The unbounded penalty greatly enhances the performance of variable selections, enabling highly accurate estimations, especially in sparse cases. Maximum likelihood estimation is effective in enabling sparse variable selection. We also study an adaptive penalty selection method to maintain a good prediction performance in cases where the variable selection is ineffective.

Suggested Citation

  • Lee, Youngjo & Oh, Hee-Seok, 2014. "A new sparse variable selection via random-effect model," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 89-99.
    • Handle: RePEc:eee:jmvana:v:125:y:2014:i:c:p:89-99
    DOI: 10.1016/j.jmva.2013.11.016
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. 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.
    3. Jianqing Fan, 1997. "Comments on «Wavelets in statistics: A review» by A. Antoniadis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 6(2), pages 131-138, August.
    4. Youngjo Lee & John A. Nelder, 2006. "Double hierarchical generalized linear models (with discussion)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 139-185, April.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    7. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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    Citations

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    Cited by:

    1. Lee, Sangin & Pawitan, Yudi & Lee, Youngjo, 2015. "A random-effect model approach for group variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 147-157.
    2. Zhixuan Fu & Chirag R. Parikh & Bingqing Zhou, 2017. "Penalized variable selection in competing risks regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 353-376, July.
    3. 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.
    4. Lee Youngjo & Gwangsu Kim, 2020. "Properties of h‐Likelihood Estimators in Clustered Data," International Statistical Review, International Statistical Institute, vol. 88(2), pages 380-395, August.
    5. Eunyoung Park & Sookhee Kwon & Jihoon Kwon & Richard Sylvester & Il Do Ha, 2020. "Penalized h‐likelihood approach for variable selection in AFT random‐effect models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(1), pages 52-71, February.
    6. 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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