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Relaxed Lasso

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  • Meinshausen, Nicolai

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  • Meinshausen, Nicolai, 2007. "Relaxed Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 374-393, September.
    • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:374-393
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    References listed on IDEAS

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    1. 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.
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    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. Ruth M. Pfeiffer & Andrew Redd & Raymond J. Carroll, 2017. "On the impact of model selection on predictor identification and parameter inference," Computational Statistics, Springer, vol. 32(2), pages 667-690, June.
    3. Radchenko, Peter, 2015. "High dimensional single index models," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 266-282.
    4. Bergersen, Linn Cecilie & Tharmaratnam, Kukatharmini & Glad, Ingrid K., 2014. "Monotone splines lasso," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 336-351.
    5. 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.
    6. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    7. Peng, Liang & Qi, Yongcheng & Wang, Ruodu, 2014. "Empirical likelihood test for high dimensional linear models," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 85-90.
    8. Min, Aleksey & Holzmann, Hajo & Czado, Claudia, 2010. "Model selection strategies for identifying most relevant covariates in homoscedastic linear models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3194-3211, December.
    9. Ollier, Edouard & Samson, Adeline & Delavenne, Xavier & Viallon, Vivian, 2016. "A SAEM algorithm for fused lasso penalized NonLinear Mixed Effect Models: Application to group comparison in pharmacokinetics," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 207-221.
    10. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
    11. Abdallah Mkhadri & Mohamed Ouhourane, 2015. "A group VISA algorithm for variable selection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 41-60, March.
    12. Jiang, Liewen & Bondell, Howard D. & Wang, Huixia Judy, 2014. "Interquantile shrinkage and variable selection in quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 208-219.
    13. Gestel, R.V. & Müller, T. & Bosmans, J., 2016. "Does My High Blood Pressure Improve Your Survival? Overall and Subgroup Learning Curves in Health," Health, Econometrics and Data Group (HEDG) Working Papers 16/27, HEDG, c/o Department of Economics, University of York.
    14. Sunyoung Shin & Sündüz Keleş, 2017. "Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 50-72, June.
    15. Roberts, S. & Nowak, G., 2014. "Stabilizing the lasso against cross-validation variability," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 198-211.

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