Cross-validation for selecting a model selection procedure

Citations

Cited by:

1. Yoonsuh Jung, 2018. "Multiple predicting K-fold cross-validation for model selection," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 197-215, January.
2. Admassu N. Lamu, 2020. "Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(6), pages 903-915, August.
3. Zhang, Yongli & Rolling, Craig & Yang, Yuhong, 2021. "Estimating and forecasting dynamic correlation matrices: A nonlinear common factor approach," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
4. Bradley Efron, 2021. "Resampling Plans and the Estimation of Prediction Error," Stats, MDPI, vol. 4(4), pages 1-25, December.
5. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
6. Wang, Sheng & Zimmerman, Dale L. & Breheny, Patrick, 2020. "Sparsity-regularized skewness estimation for the multivariate skew normal and multivariate skew t distributions," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
7. Sophie van Huellen & Duo Qin, 2019. "Compulsory Schooling and Returns to Education: A Re-Examination," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
   • Sophie van Huellen & Duo Qin, 2016. "Compulsory Schooling and the Returns to Education: A Re-examination," Working Papers 199, Department of Economics, SOAS University of London, UK.
8. Qiang Shang & Ciyun Lin & Zhaosheng Yang & Qichun Bing & Xiyang Zhou, 2016. "A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
9. Chamay Kruger & Willem Daniel Schutte & Tanja Verster, 2021. "Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions," Risks, MDPI, vol. 9(11), pages 1-26, November.
10. Huang, Y.W. & Chen, M.Q. & Li, Y. & Guo, J., 2016. "Modeling of chemical exergy of agricultural biomass using improved general regression neural network," Energy, Elsevier, vol. 114(C), pages 1164-1175.
11. Jan Szczegielniak & Krzysztof J Latawiec & Jacek Łuniewski & Rafał Stanisławski & Katarzyna Bogacz & Marcin Krajczy & Marek Rydel, 2018. "A study on nonlinear estimation of submaximal effort tolerance based on the generalized MET concept and the 6MWT in pulmonary rehabilitation," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.
12. Yuxun Wang & Liang Fang & Chao Liu & Lanxin Wang & Huimei Xu, 2023. "The Influential Factors of the Habitat Quality of the Red-crowned Crane: A Case Study of Yancheng, Jiangsu Province, China," Land, MDPI, vol. 12(6), pages 1-20, June.
13. Wang, Dieter & Andrée, Bo Pieter Johannes & Chamorro, Andres Fernando & Spencer, Phoebe Girouard, 2022. "Transitions into and out of food insecurity: A probabilistic approach with panel data evidence from 15 countries," World Development, Elsevier, vol. 159(C).
14. Fang, Fang & Li, Jialiang & Xia, Xiaochao, 2022. "Semiparametric model averaging prediction for dichotomous response," Journal of Econometrics, Elsevier, vol. 229(2), pages 219-245.
15. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
16. Yuchen Chen & Yuhong Yang, 2021. "The One Standard Error Rule for Model Selection: Does It Work?," Stats, MDPI, vol. 4(4), pages 1-25, November.
17. Peng, Jingfu & Yang, Yuhong, 2022. "On improvability of model selection by model averaging," Journal of Econometrics, Elsevier, vol. 229(2), pages 246-262.
18. Dongyoung Kim & Sungwon Jung & Yongwook Jeong, 2021. "Theft Prediction Model Based on Spatial Clustering to Reflect Spatial Characteristics of Adjacent Lands," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
19. Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Model-based Clustering of Count Processes," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 188-211, July.
20. Wang,Dieter & Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Spencer,Phoebe Girouard, 2020. "Stochastic Modeling of Food Insecurity," Policy Research Working Paper Series 9413, The World Bank.
21. Jie Ding & Vahid Tarokh & Yuhong Yang, 2018. "Model Selection Techniques -- An Overview," Papers 1810.09583, arXiv.org.
22. Fang, Fang & Yang, Qiwei & Tian, Wenling, 2022. "Cross-validation for selecting the penalty factor in least squares model averaging," Economics Letters, Elsevier, vol. 217(C).
23. Gary Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2018. "Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation," BEA Working Papers 0156, Bureau of Economic Analysis.
24. Hossein Jargan & Abbas Rohani & Armaghan Kosari-Moghaddam, 2022. "Application of modeling techniques for energy analysis of fruit production systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2616-2639, February.
25. Sijia Xiang & Weixin Yao, 2018. "Semiparametric mixtures of nonparametric regressions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 131-154, February.
26. Wenjing Yang & Yuhong Yang, 2017. "Toward an objective and reproducible model choice via variable selection deviation," Biometrics, The International Biometric Society, vol. 73(1), pages 20-30, March.