Linear Regression with Current Status Data
针对生存时间存在第一类区间删失（当前状态数据）的情况，提出一种半参数线性回归方法，无需指定误差分布，通过构造随机筛似然和约束条件估计回归参数，并给出渐近性质。
In survival analysis, a linear model often provides an adequate approximation after a suitable transformation of the survival times and possibly of the covariates. This article proposes a semiparametric regression method for estimating the regression parameter in the linear model without specifying the distribution of the random error, where the response variable is subject to so-called case 1 interval censoring. The method uses a constructed random-sieve likelihood and constraints, combining the benefits of semiparametric likelihood with estimating equations. The estimation procedure is implemented, and the asymptotic distributions for the estimated regression parameter and for the profile likelihood ratio statistic are obtained. In addition, some model diagnostics aspects are described. Finally, the small-sample operating characteristics of the proposed method is examined via simulations, and its usefulness is illustrated on datasets from an animal tumorigenicity study and from a HIV study.