Transformed Dynamic Quantile Regression on Censored Data

References

• Box, G. E. P. and Cox, D. R. (1964), “An analysis of transformations,” Journal of the American Statistical Association, 26, 211–52.
   Google Scholar
• Buckinsky, M. (1995), “Quantile Regression, Box-Cox Transformation Model, and the U.S. Wage Structure,” Journal of Econometrics, 65, 109–154.
   Web of Science ®Google Scholar
• Chamberlain, G. (1994), “Quantile Regression, Censoring and the Structure of Wages,” in Advances in Econometrics, New York: Cambridge University Press, ed. Sims, C., pp. 171–209.
   Google Scholar
• Cox, D. R. (1972), “Regression models and life-tables,” Journal of the Royal Statistical Society: Series B, 34, 187–220.
   Web of Science ®Google Scholar
• Cox, D. R. (1975), “Partial likelihood,” Biometrika, 62, 269–76.
   Web of Science ®Google Scholar
• De Backer, M., El Ghouch, A., and Van Keilegom, I. (2017), “An Adapted Loss Function for Censored Quantile Regression,” ArXiv:1703.07975.
   Google Scholar
• Engle, R. F. and Maganelli, S. (2004), “CAViaR: Conditional autoregressive Value at Risk by regression quantiles,” Journal of Business and Economics Statistics, 22, 367–81.
   Web of Science ®Google Scholar
• Fleming, T. R. and Harrington, D. P. (2005), Counting Processes and Survival Analysis, John Wiley & Sons.
   Google Scholar
• Fygenson, M. and Ritov, Y. (1994), “Monotone Estimating Equations for Censored Data,” The Annals of Statistics, 22, 732–46.
   Web of Science ®Google Scholar
• Hosmer, D. W. and Lemeshow, S. (1999), Applied Survival Analysis: Regression Modeling of Time to Event Data, Wiley, New York.
   Google Scholar
• Huang, Y. (2010), “Quantile calculus and censored regression,” Annals of Statistics, 38, 1607–1637.
   PubMed Web of Science ®Google Scholar
• Jin, Z., Lin, D. Y., Wei, L. J., and Ying, Z. (2003), “Rank-based inference for the accelerated failure time model,” Biometrika, 90, 341–53.
   Web of Science ®Google Scholar
• Jin, Z., Ying, Z., and Wei, L. J. (2001), “A Simple Resampling Method by Perturbing the Minimand,” Biometrika, 88, 381–390.
   Web of Science ®Google Scholar
• Kim, J. P., Lu, W., Sit, T., and Ying, Z. (2013), “A unified approach to semiparametric transformation models under general biased sampling schemes,” Journal of the American Statistical Association, 108, 217–27.
   PubMed Web of Science ®Google Scholar
• Kim, J. P., Sit, T., and Ying, Z. (2016), “Accelerated failure time model under general biased sampling scheme,” Biostatistics, 17, 576–88.
   PubMed Web of Science ®Google Scholar
• Koenker, R. (2005), Quantile Regression, Cambridge University Press.
   Google Scholar
• Koenker, R. and Bassett, G. (1978), “Regression quantiles,” Econometrica, 46, 33–50.
   Web of Science ®Google Scholar
• Koenker, R., Chernozhukov, V., He, X., and Peng, L. (eds.) (2017), Handbook of Quantile Regression, Chapman & Hall/CRC Handbooks of Modern Statistical Methods.
   Google Scholar
• Koenker, R. and Hallock, K. (2001), “Quantile regression,” Journal of Economic Perspectives, 15, 143–56.
   Web of Science ®Google Scholar
• Leng, C. and Tong, X. (2014), “Censored quantile regression via Box-Cox transformation under conditional independence,” Statistica Sinica, 24, 221–49.
   Web of Science ®Google Scholar
• Mu, Y. and He, X. (2007), “Power transformation toward a linear regression quantile,” Journal of the American Statistical Association, 102, 269–79.
   Web of Science ®Google Scholar
• Peng, L. and Huang, Y. (2008), “Survival analysis with quantile regression models,” Journal of the American Statistical Association, 103, 637–49.
   Web of Science ®Google Scholar
• Peng, L., Xu, J., and Kutner, N. (2014), “Shrinkage estimation of varying covariate effects based on quantile regression,” Statistics and Computing, 24, 853–69.
   PubMed Web of Science ®Google Scholar
• Portnoy, S. (2003), “Censored regression quantiles,” Journal of the American Statistical Association, 98, 1001–12.
   Web of Science ®Google Scholar
• Powell, J. L. (1984), “Least absolute deviations estimation for the censored regression model,” Journal of Econometrics, 25, 303–25.
   Web of Science ®Google Scholar
• Powell, J. L. (1986), “Censored regression quantiles,” Journal of Econometrics, 32, 143–55.
   Web of Science ®Google Scholar
• Tsiatis, A. A. (1990), “Estimating regression parameters using linear rank tests for censored data,” The Annals of Statistics, 18, 354–72.
   Web of Science ®Google Scholar
• Wang, H. and Wang, L. (2014), “Quantile regression analysis of length-biased survival data,” Stat, 3, 31–47.
   Google Scholar
• Wang, H., Zhou, J., and Li, Y. (2013), “Variable selection for censored quantile regression,” Statisica Sinica, 23, 145–67.
   PubMed Web of Science ®Google Scholar
• Wang, H. J. and Wang, L. (2009), “Locally weighted censored quantile regression,” Journal of the American Statistical Association, 104, 1117–28.
   Web of Science ®Google Scholar
• Wei, Y. and He, X. (2006), “Conditional growth charts (with discussions),” Annals of Statistics, 34, 2069–97 and 2126–31.
   Web of Science ®Google Scholar
• Wei, Y., Pere, A., Koenker, R., and He, X. (2006), “Quantile regression methods for reference growth charts,” Statistics in Medicine, 25, 1369–82.
   PubMed Web of Science ®Google Scholar
• Wu, Y. and Yin, G. (2013), “Cure rate quantile regression for censored data with a survival fraction,” Journal of the American Statistical Association, 108, 1517–31.
   Web of Science ®Google Scholar
• Xu, G., Sit, T., Wang, L., and Huang, C.-Y. (2017), “Estimation and Inference of Quantile Regression for Survival Data under Biased Sampling,” Journal of the American Statistical Association, To appear.
   Web of Science ®Google Scholar
• Yin, G., Zeng, D., and Li, H. (2008), “Power-transformed linear quantile regression with censored data,” Journal of the American Statistical Association, 103, 1214–24.
   Web of Science ®Google Scholar
• Ying, Z. (1993), “A large sample study of rank estimation for censored regression data,” The Annals of Statistics, 21, 76–99.
   Web of Science ®Google Scholar
• Ying, Z., Jung, S. H., and Wei, L. J. (1995), “Survival analysis with median regression models,” Journal of the American Statistical Association, 90, 178–84.
   Web of Science ®Google Scholar
• Zheng, Q., Peng, L., and He, X. (2017), “High dimensional censored quantile regression,” Annals of Statistics, To appear.
   PubMed Web of Science ®Google Scholar