Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR. Variable selection can be challenging, particularly in situations with a large number of predictors with possibly high correlations, such as gene expression data. In this article, a new method, called OSCAR (octagonal shrinkage and clustering algorithm for regression), is proposed to simultaneously select variables while grouping them into predictive clusters. In addition to improving prediction accuracy and interpretation, these resulting groups can then be investigated further to discover what contributes to the group having a similar behavior. The technique is based on penalized least squares with a geometrically intuitive penalty function that shrinks some coefficients to exactly zero. Additionally, this penalty yields exact equality of some coefficients, encouraging correlated predictors that have a similar effect on the response to form predictive clusters represented by a single coefficient. The proposed procedure is shown to compare favorably to the existing shrinkage and variable selection techniques in terms of both prediction error and model complexity, while yielding the additional grouping information.

References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Jang, Woncheol; Lim, Johan; Lazar, Nicole A.; Loh, Ji Meng; Yu, Donghyeon: Some properties of generalized fused lasso and its applications to high dimensional data (2015)
  2. Narisetty, Naveen Naidu; He, Xuming: Bayesian variable selection with shrinking and diffusing priors (2014)
  3. Oiwa, Hidekazu; Matsushima, Shin; Nakagawa, Hiroshi: Feature-aware regularization for sparse online learning (2014)
  4. Yao, Yonggang; Lee, Yoonkyung: Another look at linear programming for feature selection via methods of regularization (2014)
  5. Ahn, Mihye; Zhang, Hao Helen; Lu, Wenbin: Moment-based method for random effects selection in linear mixed models (2012)
  6. Bondell, Howard D.; Reich, Brian J.: Consistent high-dimensional Bayesian variable selection via penalized credible regions (2012)
  7. Petry, Sebastian; Tutz, Gerhard: Shrinkage and variable selection by polytopes (2012)
  8. Shen, Xiaotong; Huang, Hsin-Cheng; Pan, Wei: Simultaneous supervised clustering and feature selection over a graph (2012)
  9. Zeng, Lingmin; Xie, Jun: Group variable selection for data with dependent structures (2012)
  10. Binder, Harald; Porzelius, Christine; Schumacher, Martin: An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models (2011)
  11. Ghosh, Samiran: On the grouped selection and model complexity of the adaptive elastic net (2011)
  12. Huang, Jian; Ma, Shuangge; Li, Hongzhe; Zhang, Cun-Hui: The sparse Laplacian shrinkage estimator for high-dimensional regression (2011)
  13. Porzelius, Christine: Model complexity selection in high-dimensional time-to-event data analysis (2011)
  14. Bondell, Howard D.; Krishna, Arun; Ghosh, Sujit K.: Joint variable selection for fixed and random effects in linear mixed-effects models (2010)
  15. Lee, Mihee; Shen, Haipeng; Huang, Jianhua Z.; Marron, J.S.: Biclustering via sparse singular value decomposition (2010)
  16. Ojala, Markus; Garriga, Gemma C.: Permutation tests for studying classifier performance (2010)
  17. Pan, Wei; Xie, Benhuai; Shen, Xiaotong: Incorporating predictor network in penalized regression with application to microarray data (2010)
  18. Bondell, Howard D.; Reich, Brian J.: Simultaneous factor selection and collapsing levels in ANOVA (2009)
  19. Wu, S.; Shen, X.; Geyer, C.J.: Adaptive regularization using the entire solution surface (2009)
  20. Zhu, Yanni; Pan, Wei; Shen, Xiaotong: Support vector machines with disease-gene-centric network penalty for high dimensional microarray data (2009)

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