References
- Diamond ID, McDonald JW, Shah IH. Proportional hazards models for current status data: application to the study of differentials in age at weaning in Pakistan. Demography. 1986;23:607–620. doi: 10.2307/2061354
- Diamond ID, McDonald JW. Analysis of current status data. Demogr Appl Event Hist Anal. 1992;58:231–252.
- Huang J. Efficient estimation for the proportional hazards model with interval censoring. Ann Stat. 1996;24:540–568. doi: 10.1214/aos/1032894452
- Xue H, Lam KF, Li G. Sieve maximum likelihood estimator for semiparametric regression models with current status data. J Am Stat Assoc. 2004;99:346–356. doi: 10.1198/016214504000000313
- Hu T, Cui H-J, Tong X-W. Efficient estimation for semiparametric varying-coefficient partially linear regression models with current status data. Acta Math Appl Sin Engl Ser. 2009;25(2):195–204. doi: 10.1007/s10255-008-8133-7
- Ma S, Kosorok MR. Penalized log-likelihood estimation for partly linear transformation models with current status data. Ann Stat. 2005a;33:2256–2290. doi: 10.1214/009053605000000444
- Cheng G, Wang X. Semiparametric additive transformation model under current status data. Electron J Stat. 2011;5:1735–1764. doi: 10.1214/11-EJS656
- Zhang Y, Hua L, Huang J. A spline-based semiparametric maximum likelihood estimation method for the cox model with interval-censored data. Scand J Stat. 2010;37:338–354. doi: 10.1111/j.1467-9469.2009.00680.x
- Wang Q, Tong X, Sun L. Exploring the varying covariate effects in proportional odds models with censored data. J Multivar Anal. 2012;109:168–189. doi: 10.1016/j.jmva.2012.02.013
- He X, Feng X, Tong X, et al. Semiparametric partially linear varying coefficient models with panel count data. Lifetime Data Anal. 2017;23:439–466. doi: 10.1007/s10985-016-9368-x
- Zhu R, Wan AT, Zhang X, et al. A mallows-type model averaging estimator for the varying-coefficient partially linear model. J Am Stat Assoc. 2018:1–11. DOI: 10.1080/01621459.2018.1456936.
- Xu H-X, Fan G-L, Wu C-X, et al. Statistical inference for varying-coefficient partially linear errors-in-variables models with missing data. Commun Stat-Theory Methods. 2019;5: 1–16. DOI: 10.1080/03610926.2018.1517216.
- Huang J. Efficient estimation of the partly linear additive cox model. Ann Stat. 1999;27:1536–1563. doi: 10.1214/aos/1017939141
- Grenander U. Abstract inference. New York (NY): Wiley; 1981.
- Martinussen T, Scheike TH. Efficient estimation in additive hazards regression with current status data. Biometrika. 2002;89:649–658. doi: 10.1093/biomet/89.3.649
- Bickel PJ, Klaassen CA, Ritov Y, et al. Efficient and adaptive estimation for semiparametric models. Baltimore (MD): The Johns Hopkins University Press; 1993.
- Stone CJ. Additive regression and other nonparametric models. Ann Stat. 1985;13:689–705. doi: 10.1214/aos/1176349548
- Gray RJ. Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. J Am Stat Assoc. 1992;87:942–951. doi: 10.1080/01621459.1992.10476248
- Ma S, Kosorok MR. Robust semiparametric m-estimation and the weighted bootstrap. J Multivar Anal. 2005b;96:190–217. doi: 10.1016/j.jmva.2004.09.008
- Heung M, Wolfgram DF, Kommareddi M, et al. Fluid overload at initiation of renal replacement therapy is associated with lack of renal recovery in patients with acute kidney injury. Nephrol Dial Transplant. 2012;27:956–961. doi: 10.1093/ndt/gfr470