Bivariate Estimation with Right-Truncated Data
右截断数据的二元估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要聚焦右截断生存数据的二元非参数估计，缺少与所列经济计量细分方向直接对应的应用或模型依据。
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右截断数据的二元估计
- 研究问题
- 当二元变量中的生存时间受到随机右截断、只能观察满足截断条件的样本时，如何估计二元生存函数及其不确定性？
- 方法与思路
- 作者引入二元反向风险率向量及非参数估计量，提出二元生存函数估计，并利用独立同分布变量表示开展渐近分析。
- 材料陈述的贡献
- 摘要称，生存函数估计具有弱收敛与强一致性，并给出极限协方差和方差估计；其他分布函数方法通过模拟比较。
- 阅读关注点
- 应区分生存函数与分布函数估计各自获得的理论结果，核对截断独立性条件、变量表示及极限方差估计的适用范围。
- 阅读边界
- 材料为 OpenAlex 索引摘要，稿件版本未注明；摘要未给出完整条件与模拟数值，不能把生存函数的渐近结论直接移用于所有替代方法。
原文摘要
Bivariate estimation with survival data has received considerable attention recently; however, most of the work has focused on random censoring models. Another common feature of survival data, random truncation, is considered in this study. Truncated data may arise if the time origin of the events under study precedes the observation period. In a random right-truncation model, one observes the iid samples of (Y, T) only if (Y ≤ T), where Y is the variable of interest and T is an independent variable that prevents the complete observation of Y. Suppose that (Y, X) is a bivariate vector of random variables, where Y is subject to right truncation. In this study the bivariate reverse-hazard vector is introduced, and a nonparametric estimator is suggested. An estimator for the bivariate survival function is also proposed. Weak convergence and strong consistency of this estimator are established via a representation by iid variables. An expression for the limiting covariance function is provided, and an estimator for the limiting variance is presented. Alternative methods for estimating the bivariate distribution function are discussed. Obtaining large-sample results for the bivariate distribution functions present more technical difficulties, and thus their performances are compared via simulation results. Finally, an application of the suggested estimators is presented for transfusion-related AIDS (TR-AIDS) data on the incubation time.