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Fast sparse regression and classification

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  • Friedman, Jerome H.

Abstract

Many present day applications of statistical learning involve large numbers of predictor variables. Often, that number is much larger than the number of cases or observations available for training the learning algorithm. In such situations, traditional methods fail. Recently, new techniques have been developed, based on regularization, which can often produce accurate models in these settings. This paper describes the basic principles underlying the method of regularization, then focuses on those methods which exploit the sparsity of the predicting model. The potential merits of these methods are then explored by example.

Suggested Citation

  • Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:3:p:722-738
    DOI: 10.1016/j.ijforecast.2012.05.001
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    References listed on IDEAS

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    Cited by:

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    2. Ting‐Huei Chen & Hanaa Boughal, 2021. "A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease," Biometrics, The International Biometric Society, vol. 77(1), pages 362-371, March.
    3. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    4. 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.
    5. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    6. Nir Billfeld & Moshe Kim, 2019. "Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision," Papers 1902.06286, arXiv.org.
    7. Yu-Fan Li & Kun Shang & Zheng-Hai Huang, 2019. "A singular value p-shrinkage thresholding algorithm for low rank matrix recovery," Computational Optimization and Applications, Springer, vol. 73(2), pages 453-476, June.
    8. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
    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.
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