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删失数据中二元生存曲线的可识别性AI 解读

We show that the survival curve is identifiable in bivariate censored data problems under weaker independence assumptions than have commonly been made. The common assumption has been mutual independence of (T 1, T 2) and (Z 1, Z 2), where (T 1, T 2) is the true survival vector, (Z 1, Z 2) is a nuisance censoring vector, and bivariate right-censored data is observed. We show that the distribution of (T 1, T 2) is identifiable under weaker, conditional independence assumptions…

删失数据中双变量生存曲线的可识别性AI 解读

We show that the survival curve is identifiable in bivariate censored data problems under weaker independence assumptions than have commonly been made. The common assumption has been mutual independence of (T 1, T 2) and (Z 1, Z 2), where (T 1, T 2) is the true survival vector, (Z 1, Z 2) is a nuisance censoring vector, and bivariate right-censored data is observed. We show that the distribution of (T 1, T 2) is identifiable under weaker, conditional independence assumptions…

二元 Kaplan–Meier 估计量赋予的负质量AI 解读

Conditions under which the bivariate Kaplan-Meier estimate of Dabrowska is not a proper survival function are given. All points assigned negative mass are identified under the assumption that the observations follow an absolutely continuous distribution. The number of points assigned negative mass increases as $n^2$ and the total amount of negative mass does not disappear as $n \rightarrow \infty$, where $n$ is the sample size. A simulation study is reported which shows that…

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