Penalized full likelihood approach to variable selection for Cox’s regression model under nested case–control sampling
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DOI: 10.1007/s10985-019-09475-z
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References listed on IDEAS
- Zhao, Sihai Dave & Li, Yi, 2012. "Principled sure independence screening for Cox models with ultra-high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 397-411.
- Ørnulf Borgan & Ying Zhang, 2015. "Using cumulative sums of martingale residuals for model checking in nested case‐control studies," Biometrics, The International Biometric Society, vol. 71(3), pages 696-703, September.
- Ai Ni & Jianwen Cai & Donglin Zeng, 2016. "Variable selection for case-cohort studies with failure time outcome," Biometrika, Biometrika Trust, vol. 103(3), pages 547-562.
- 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.
- Thomas H. Scheike & Torben Martinussen, 2004. "Maximum Likelihood Estimation for Cox's Regression Model Under Case–Cohort Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 283-293, June.
- Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
- Sihai Dave Zhao & Yi Li, 2014. "Score test variable screening," Biometrics, The International Biometric Society, vol. 70(4), pages 862-871, December.
- Kani Chen, 2001. "Generalized case–cohort sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 791-809.
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Keywords
Nested case–control sampling; Oracle property; PNPMLE; SCAD;All these keywords.
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