Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods
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DOI: 10.1080/01621459.2013.858630
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References listed on IDEAS
- Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
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- 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.
- Qing Mai & Hui Zou & Ming Yuan, 2012. "A direct approach to sparse discriminant analysis in ultra-high dimensions," Biometrika, Biometrika Trust, vol. 99(1), pages 29-42.
- Hall, Peter & Titterington, D. M. & Xue, Jing-Hao, 2009. "Median-Based Classifiers for High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1597-1608.
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Cited by:
- Hang Yu & Yuanjia Wang & Donglin Zeng, 2023. "A general framework of nonparametric feature selection in high‐dimensional data," Biometrics, The International Biometric Society, vol. 79(2), pages 951-963, June.
- Doksum, Kjell A. & Jiang, Jiancheng & Sun, Bo & Wang, Shuzhen, 2017. "Nearest neighbor estimates of regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 64-74.
- Kyle R. White & Leonard A. Stefanski & Yichao Wu, 2017. "Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1587-1597, October.
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