Buckley–James-type estimation of quantile regression with recurrent gap time data

References

• D.F. Byar, The veterans administration study of chemoprophylaxis for recurrent stage I bladder tumors: comparisons of placebo, pyridoxine, and topical thiotepa, in Bladder Tumors and Other Topics in Urological Oncology, M. Pavone-Macaluso, P.H. Smith, and F. Edsmyn, eds., Plenum, New York, 1980, pp. 363–370.
   Google Scholar
• S.-H. Chang, Estimating marginal effects in accelerated failure time models for serial sojourn times among repeated events, Lifetime Data Anal. 10 (2004), pp. 175–190. doi: 10.1023/B:LIDA.0000030202.20842.c9
   PubMed Web of Science ®Google Scholar
• B. Efron, The two-sample problem with censored data, in Proceedings Fifth Berkley Symposium in Mathematical Statistics, Vol. IV, L. Le Cam and J. Neyman, eds., Prentice-Hall, New York, 1967, pp. 831–853.
   Google Scholar
• Y. Huang and Q. Chen, Marginal regression of gaps between recurrent events, Lifetime Data Anal. 9 (2003), pp. 293–303. doi: 10.1023/A:1025892922453
   PubMed Web of Science ®Google Scholar
• Z. Jin, D.Y. Lin, and Z. Ying, On least-squares regression with censored data, Biometrika 93 (2006), pp. 147–161. doi: 10.1093/biomet/93.1.147
   Web of Science ®Google Scholar
• Z. Jin, Z. Ying, and L.J. Wei, A simple resampling method by perturbing the minimand, Biometrika 88 (2001), pp. 381–390. doi: 10.1093/biomet/88.2.381
   Web of Science ®Google Scholar
• R. Koenker and G. Bassett, Regression quantiles, Econometrica 46 (1978), pp. 33–50. doi: 10.2307/1913643
   Web of Science ®Google Scholar
• X. Luo and C.-Y. Huang, Analysis of recurrent gap time data using the weighted risk-set method and the modified withincluster resampling method, Statist. Med. 30 (2011), pp. 301–311. doi: 10.1002/sim.4074
   PubMed Web of Science ®Google Scholar
• X. Luo, C.-Y. Huang, and L. Wang, Quantile regression for recurrent gap time data, Biometrics 69 (2013), pp. 375–385. doi: 10.1111/biom.12010
   PubMed Web of Science ®Google Scholar
• J. Ning, J. Qin, and Y. Shen, Buckley-James-type estimator with right-censored and length-biased data, Biometrics 67 (2011), pp. 1369–1378. doi: 10.1111/j.1541-0420.2011.01568.x
   PubMed Web of Science ®Google Scholar
• L. Peng, Self-consistent estimation of censored quantile regression, J. Multivariate Anal. 105 (2012), pp. 368–379. doi: 10.1016/j.jmva.2011.10.005
   Web of Science ®Google Scholar
• L. Peng and P. Fine, Competing risks quantile regression, J. Amer. Statist. Assoc. 104 (2009), pp. 1440–1453. doi: 10.1198/jasa.2009.tm08228
   Web of Science ®Google Scholar
• L. Peng and Y. Huang, Survival analysis with quantile regression models, J. Amer. Statist. Assoc. 103 (2008), pp. 637–649. doi: 10.1198/016214508000000355
   Web of Science ®Google Scholar
• S. Portnoy, Censored regression quantiles, J. Amer. Statist. Assoc. 98 (2003), pp. 1001–1012. doi: 10.1198/016214503000000954
   Web of Science ®Google Scholar
• J.L. Powell, Least absolute deviations estimation for the censored regression model, J. Econom. 25 (1984), pp. 303–325. doi: 10.1016/0304-4076(84)90004-6
   Web of Science ®Google Scholar
• J.L. Powell, Censored regression quantiles, J. Econom. 32 (1986), pp. 143–155. doi: 10.1016/0304-4076(86)90016-3
   Web of Science ®Google Scholar
• J. Robins and A. Rotnitzky, Recovery of information and adjustment for dependent censoring using surrogate markers, in AIDS Epidemiology–Methodological Issues, N.P. Jewell, K. Dietz, and B. Farewell, eds., Birkhauser, Boston, 1992, pp. 297–331.
   Google Scholar
• R.L. Strawderman, The accelerated gap times model, Biometrika 92 (2005), pp. 647–666. doi: 10.1093/biomet/92.3.647
   Web of Science ®Google Scholar
• L.Q. Sun, D.H. Park, and J.G. Sun, The additive hazards model for recurrent gap times, Statist. Sin. 16 (2006), pp. 919–932.
   Web of Science ®Google Scholar
• M.-C. Wang and S.-H. Chang, Nonparametric estimation of a recurrent survival function, J. Amer. Statist. Assoc. 94 (1999), pp. 146–153. doi: 10.1080/01621459.1999.10473831
   PubMed Web of Science ®Google Scholar
• H.J. Wang and L. Wang, Locally weighted censored quantile regression, J. Amer. Statist. Assoc. 104 (2009), pp. 1117–1128. doi: 10.1198/jasa.2009.tm08230
   Web of Science ®Google Scholar
• L.J. Wei, D.Y. Lin, and L. Weissfeld, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, J. Amer. Statist. Assoc. 84 (1989), pp. 1065–1073. doi: 10.1080/01621459.1989.10478873
   Web of Science ®Google Scholar
• S. Yang, Censoring median regression using weighted empirical survival and hazard functions, J. Amer. Statist. Assoc. 94 (1999), pp. 137–145. doi: 10.1080/01621459.1999.10473830
   Web of Science ®Google Scholar
• G. Yin and J. Cai, Quantile regression models with multivariate failure time data, Biometrics 61 (2005), pp. 151–161. doi: 10.1111/j.0006-341X.2005.030815.x
   PubMed Web of Science ®Google Scholar
• Z. Ying, S.H. Jung, and L.J. Wei, Survival analysis with median regression models, J. Amer. Statist. Assoc. 90 (1995), pp. 178–184. doi: 10.1080/01621459.1995.10476500
   Web of Science ®Google Scholar