Review Articles

Discussion of ‘A selective review of statistical methods using calibration information from similar studies’

Jing Ning

The University of Texas MD Anderson Cancer Center, Houston, TX, USA

jning@mdanderson.org

Pages | Received 30 Apr. 2022, Accepted 02 May. 2022, Published online: 15 May. 2022,
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  • Chen, Z., Ning, J., Shen, Y., & Qin, J. (2021). Combining primary cohort data with external aggregate information without assuming comparability. Biometrics77(3), 1024–1036. https://doi.org/10.1111/biom.v77.3
  • Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association96(456), 1348–1360. https://doi.org/10.1198/016214501753382273
  • Huang, C.-Y., Qin, J., & Tsai, H.-T. (2016). Efficient estimation of the Cox model with auxiliary subgroup survival information. Journal of the American Statistical Association111(514), 787–799. https://doi.org/10.1080/01621459.2015.1044090 
  • Kai, B., Li, R., & Zou, H. (2011). New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models. Annals of Statistics39(1), 305–332. https://doi.org/10.1214/10-AOS842 
  • Li, S., Cai, T. T., & Li, H. (2022). Transfer learning for high-dimensional linear regression: prediction, estimation, and minimax optimality. Journal of the Royal Statistical Society. Series B, Statistical Methodology84(1), 149–173. https://doi.org/10.1111/rssb.v84.1
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x 
  • Wang, H., Li, R., & Tsai, C. L. (2007). Tuning parameter selectors for the smoothly clipped absolute deviation method. Biometrika94(3), 553–568. https://doi.org/10.1093/biomet/asm053