但计算很是困难，原因就是删失数据后样本的似然函数的形式很复杂。
But it was difficult to calculate, because censored data in the form of samples of the likelihood function is very complex.
传统的乘积限估计法只能处理寿终数据和右删失数据，对左截断数据则无能为力。
The traditional product limit estimation method can only deal with complete data and right censored data, but can not deal with left truncated data.
采用逆概率删失加权分析(IPCW)模型以更好地评估转换治疗的相对治疗效果。
To gain better estimates of relative treatment effects in the presence of selective crossover, we used inverse probability of censoring weighted (IPCW) modeling.
基于K型区间删失数据，利用样本空间排序法给出参数优良的置信下限和计算置信下限的递推公式。
In the case of K type interval censored data, the lower confidence limit of parameter is studied based on the order relation established in the sample space.
当生命数据是离散的、未删失数据含有打结的和有协变量信息时，离散生存分析模型是适当的选择。
Discrete-time survival model is appropriate as survival data are discrete, tied and some effects for covariates are added.
在左截断右删失数据下，我们基于乘积限估计给出了分位密度估计，获得了分位密度估计及其导数的重对数律。
In this paper laws of the iterated logarithm for quantile density estimator and its derivative estimators are established when data are subject to left-truncated and right-censored observations.
结合实例说明了含有删失数据的可靠性函数估计的几种方法，对于现场寿命数据分析与可靠性评估具有参考价值。
Illustrated with examples, it has reference value for the field life data analysis and reliability evaluation, illustrating several estimation methods concerning reliability function of deleted data.
基于左截断右删失数据下的乘积限估计构造了分位数固定宽度序贯置信区间及其估计，研究了序贯置信区间估计的渐近性质。
The Bahadur representations for this quantile estimator are established in order to derive asymptotic properties of the sequential fixed-width confidence intervals estimation for quantiles.
基于左截断右删失数据下的乘积限估计构造了分位数固定宽度序贯置信区间及其估计，研究了序贯置信区间估计的渐近性质。
The Bahadur representations for this quantile estimator are established in order to derive asymptotic properties of the sequential fixed-width confidence intervals estimation for quantiles.
应用推荐