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Variable Selection Using Adaptive Nonlinear Interaction Structures in High Dimensions

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  • Radchenko, Peter
  • James, Gareth M.

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  • Radchenko, Peter & James, Gareth M., 2010. "Variable Selection Using Adaptive Nonlinear Interaction Structures in High Dimensions," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1541-1553.
    • Handle: RePEc:bes:jnlasa:v:105:i:492:y:2010:p:1541-1553
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    Cited by:

    1. Du, Pang & Cheng, Guang & Liang, Hua, 2012. "Semiparametric regression models with additive nonparametric components and high dimensional parametric components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2006-2017.
    2. Giordano, Francesco & Parrella, Maria Lucia, 2016. "Bias-corrected inference for multivariate nonparametric regression: Model selection and oracle property," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 71-93.
    3. Li Yun & O’Connor George T. & Dupuis Josée & Kolaczyk Eric, 2015. "Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(3), pages 265-277, June.
    4. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
    5. Zaili Fang & Inyoung Kim & Patrick Schaumont, 2016. "Flexible variable selection for recovering sparsity in nonadditive nonparametric models," Biometrics, The International Biometric Society, vol. 72(4), pages 1155-1163, December.
    6. Dewei Zhang & Yin Liu & Sam Davanloo Tajbakhsh, 2022. "A First-Order Optimization Algorithm for Statistical Learning with Hierarchical Sparsity Structure," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1126-1140, March.
    7. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    8. Yawei He & Zehua Chen, 2016. "The EBIC and a sequential procedure for feature selection in interactive linear models with high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 155-180, February.
    9. Gregor Stiglic & Petra Povalej Brzan & Nino Fijacko & Fei Wang & Boris Delibasic & Alexandros Kalousis & Zoran Obradovic, 2015. "Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-11, December.
    10. Wang, Lu & Shen, Jincheng & Thall, Peter F., 2014. "A modified adaptive Lasso for identifying interactions in the Cox model with the heredity constraint," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 126-133.
    11. Feng Li & Yajie Li & Sanying Feng, 2021. "Estimation for Varying Coefficient Models with Hierarchical Structure," Mathematics, MDPI, vol. 9(2), pages 1-18, January.
    12. Radchenko, Peter, 2015. "High dimensional single index models," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 266-282.
    13. Bhatnagar, Sahir R. & Lu, Tianyuan & Lovato, Amanda & Olds, David L. & Kobor, Michael S. & Meaney, Michael J. & O'Donnell, Kieran & Yang, Archer Y. & Greenwood, Celia M.T., 2023. "A sparse additive model for high-dimensional interactions with an exposure variable," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    14. Fabian Scheipl & Thomas Kneib & Ludwig Fahrmeir, 2013. "Penalized likelihood and Bayesian function selection in regression models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 349-385, October.
    15. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
    16. Yao Dong & He Jiang, 2018. "A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model," Complexity, Hindawi, vol. 2018, pages 1-12, November.
    17. Francesco Giordano & Soumendra Nath Lahiri & Maria Lucia Parrella, 2014. "GRID for model structure discovering in high dimensional regression," Working Papers 3_231, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
    18. Xi Wu & Shifeng Xiong & Weiyan Mu, 2023. "An Ensemble Method for Feature Screening," Mathematics, MDPI, vol. 11(2), pages 1-14, January.
    19. Jonathan Boss & Alexander Rix & Yin‐Hsiu Chen & Naveen N. Narisetty & Zhenke Wu & Kelly K. Ferguson & Thomas F. McElrath & John D. Meeker & Bhramar Mukherjee, 2021. "A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    20. Han Li & Yashu Liu & Pinghua Gong & Changshui Zhang & Jieping Ye & for the Alzheimers Disease Neuroimaging Initiative, 2014. "Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) Conversion," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    21. Oliver J. Rutz & Garrett P. Sonnier, 2019. "VANISH regularization for generalized linear models," Quantitative Marketing and Economics (QME), Springer, vol. 17(4), pages 415-437, December.
    22. Jacob Bien & Florentina Bunea & Luo Xiao, 2016. "Convex Banding of the Covariance Matrix," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 834-845, April.

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