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Incorporating Predictor Network in Penalized Regression with Application to Microarray Data

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  • Wei Pan
  • Benhuai Xie
  • Xiaotong Shen

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  • Wei Pan & Benhuai Xie & Xiaotong Shen, 2010. "Incorporating Predictor Network in Penalized Regression with Application to Microarray Data," Biometrics, The International Biometric Society, vol. 66(2), pages 474-484, June.
    • Handle: RePEc:bla:biomet:v:66:y:2010:i:2:p:474-484
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2009.01296.x
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    References listed on IDEAS

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    1. Howard D. Bondell & Brian J. Reich, 2008. "Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR," Biometrics, The International Biometric Society, vol. 64(1), pages 115-123, March.
    2. 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.
    3. 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.
    4. 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.
    5. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

    1. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2015. "miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer," Biometrics, The International Biometric Society, vol. 71(2), pages 428-438, June.
    2. Sunkyung Kim & Wei Pan & Xiaotong Shen, 2013. "Network‐Based Penalized Regression With Application to Genomic Data," Biometrics, The International Biometric Society, vol. 69(3), pages 582-593, September.
    3. Bergersen Linn Cecilie & Glad Ingrid K. & Lyng Heidi, 2011. "Weighted Lasso with Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-29, August.
    4. Sandra E. Safo & Shuzhao Li & Qi Long, 2018. "Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information," Biometrics, The International Biometric Society, vol. 74(1), pages 300-312, March.
    5. Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.
    6. Liu, Jianyu & Yu, Guan & Liu, Yufeng, 2019. "Graph-based sparse linear discriminant analysis for high-dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 250-269.
    7. Long Qu & Dan Nettleton & Jack C. M. Dekkers, 2012. "A Hierarchical Semiparametric Model for Incorporating Intergene Information for Analysis of Genomic Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1168-1177, December.
    8. Yi Zhao & Bingkai Wang & Chin‐Fu Liu & Andreia V. Faria & Michael I. Miller & Brian S. Caffo & Xi Luo, 2023. "Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors," Biometrics, The International Biometric Society, vol. 79(3), pages 2333-2345, September.
    9. Yanhang Zhang & Junxian Zhu & Jin Zhu & Xueqin Wang, 2023. "A Splicing Approach to Best Subset of Groups Selection," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 104-119, January.

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