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Boosting ridge regression

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  • Tutz, Gerhard
  • Binder, Harald

Abstract

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  • Tutz, Gerhard & Binder, Harald, 2007. "Boosting ridge regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6044-6059, August.
  • Handle: RePEc:eee:csdana:v:51:y:2007:i:12:p:6044-6059
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    References listed on IDEAS

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    1. Buhlmann P. & Yu B., 2003. "Boosting With the L2 Loss: Regression and Classification," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 324-339, January.
    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.
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    Citations

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    Cited by:

    1. Faisal Zahid & Gerhard Tutz, 2013. "Multinomial logit models with implicit variable selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(4), pages 393-416, December.
    2. Belitz, Christiane & Lang, Stefan, 2008. "Simultaneous selection of variables and smoothing parameters in structured additive regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 61-81, September.
    3. Wang Zhu & Wang C.Y., 2010. "Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
    4. Binder Harald & Müller Tina & Schwender Holger & Golka Klaus & Steffens Michael & Hengstler Jan G. & Ickstadt Katja & Schumacher Martin, 2012. "Cluster-Localized Sparse Logistic Regression for SNP Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-31, August.
    5. Christmann, Andreas & Hable, Robert, 2012. "Consistency of support vector machines using additive kernels for additive models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 854-873.
    6. Blagus, Rok & Lusa, Lara, 2017. "Gradient boosting for high-dimensional prediction of rare events," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 19-37.
    7. Riccardo De Bin, 2016. "Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost," Computational Statistics, Springer, vol. 31(2), pages 513-531, June.
    8. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
    9. Sariyar Murat & Schumacher Martin & Binder Harald, 2014. "A boosting approach for adapting the sparsity of risk prediction signatures based on different molecular levels," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 1-15, June.
    10. Jing Zeng, 2014. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Working Paper Series of the Department of Economics, University of Konstanz 2014-20, Department of Economics, University of Konstanz.

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