On assessing the association for bivariate current status data
On assessing the association for bivariate current status data
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DOI：
10.1093/biomet/87.4.879
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发表时间：
2000-12-01
期刊：
影响因子：
2.7
通讯作者：
Ding, AA
中科院分区：
文献类型：
--
作者：
Wang, WJ;Ding, AA
假设双变量当前状态数据的两个失效时间服从双变量Copula模型，我们提出了一个两阶段估计方法来估计与Kendall‘s tau相关的关联参数。所提出的半参数估计的渐近性质表明，虽然第一阶段边缘估计的收敛速度仅为n(1/3)，但所得到的参数估计仍然以通常的n(1/2)速度收敛到正态随机变量。所提出的估计量的方差可以被一致地估计。给出了模拟结果，并提供了一个以社区为基础的台湾心血管疾病研究的例子。
Assuming that the two failure times of interest with bivariate current status data follow a bivariate copula model, we propose a two-stage estimation procedure to estimate the association parameter which is related to Kendall's tau. Asymptotic properties of the proposed semiparametric estimator show that, although the first-stage marginal estimators have a convergence rate of only n(1/3), the resulting parameter estimator still converges to a normal random variable with the usual n(1/2) rate. The variance of the proposed estimator can be consistently estimated. Simulation results are presented, and a community-based study of cardiovascular diseases in Taiwan provides an illustrative example.