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My bibliography Save this articleCross-validation for selecting a model selection procedure
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DOI: 10.1016/j.jeconom.2015.02.006
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- T. Speed & Bin Yu, 1993. "Model selection and prediction: Normal regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 35-54, March.
- Yuhong Yang, 2005. "Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation," Biometrika, Biometrika Trust, vol. 92(4), pages 937-950, December.
- Yang Y., 2001. "Adaptive Regression by Mixing," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 574-588, June.
- Jianqing Fan & Jinchi Lv & Lei Qi, 2011. "Sparse High-Dimensional Models in Economics," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 291-317, September.
- Tim van Erven & Peter Grünwald & Steven de Rooij, 2012. "Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC–BIC dilemma," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 361-417, June.
- Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
- Vaart Aad W. van der & Dudoit Sandrine & Laan Mark J. van der, 2006. "Oracle inequalities for multi-fold cross validation," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 1-21, December.
- Yang, Yuhong, 2007. "Prediction/Estimation With Simple Linear Models: Is It Really That Simple?," Econometric Theory, Cambridge University Press, vol. 23(1), pages 1-36, February.
- Shen X. & Ye J., 2002. "Adaptive Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 210-221, March.
- Andrews, Donald W. K., 1991. "Asymptotic optimality of generalized CL, cross-validation, and generalized cross-validation in regression with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 359-377, February.
- 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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Keywords
Cross-validation; Cross-validation paradox; Data splitting ratio; Adaptive procedure selection; Information criterion; LASSO; MCP; SCAD;All these keywords.
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