Variable-selection consistency of linear quantile regression by validation set approach
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DOI: 10.1016/j.spl.2025.110431
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- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Koenker,Roger, 2005.
"Quantile Regression,"
Cambridge Books,
Cambridge University Press, number 9780521845731, Enero-Abr.
- Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, May.
- 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.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- He, Xuming & Shao, Qi-Man, 2000. "On Parameters of Increasing Dimensions," Journal of Multivariate Analysis, Elsevier, vol. 73(1), pages 120-135, April.
- Muhammad Amin & Lixin Song & Milton Abdul Thorlie & Xiaoguang Wang, 2015. "SCAD-penalized quantile regression for high-dimensional data analysis and variable selection," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 212-235, August.
- Lan Wang & Yichao Wu & Runze Li, 2012. "Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 214-222, March.
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