A Bivariate Competing-Risks Model with One Termination Event

On the identifiability of copulas in bivariate competing risks models

In competing risks models, the joint distribution of the event times is not identifiable even when the margins are fully known, which has been referred to as the "identifiability crisis in competing risks analysis" (Crowder, 1991). We model the dependence between the event times by an unknown copula and show that identification is actually possible within many frequently used families of copulas. The result is then extended to the case where one margin is unknown.

具有竞争风险的二元生存时间数据的模型可识别性：非参数特定原因风险与Gamma脆弱性

Model Identifiability for Bivariate Failure Time Data with Competing Risk: Non-parametric Cause-specific Hazards and Gamma Frailty

本研究探讨了带有竞争风险的二元生存数据在非参数基线特定原因风险和不同Gamma脆弱性分布下的模型可识别性，证明了双非参数模型的不可识别性，并验证了四种Gamma脆弱性模型在一般假设下的可识别性。

含竞争风险的双变量失效时间数据的模型可识别性：参数化原因别风险与非参数脆弱性

Model Identifiability for Bivariate Failure Time Data with Competing Risks: Parametric Cause-specific Hazards and Non-parametric Frailty

本文针对含竞争风险的双变量生存数据，结合四类非参数脆弱性与参数化基线原因别风险函数，研究模型可识别性，并假设脆弱性均值为1。

使用 LINEX 损失函数从二元正态总体中进行选择后的估计

Estimation after selection from bivariate normal population using LINEX loss function

本文研究在 LINEX 损失函数下从两个独立二元正态总体中选择最佳总体后的特征估计问题，提出了自然型估计量和 Bayes 估计量，推导了改进竞争估计量的充分条件，并通过真实数据和模拟研究验证了所提估计量的优越性。

通过矩阵分布建模离散共同冲击风险

Modeling discrete common-shock risks through matrix distributions

本文提出了一种双变量共同冲击离散相位型分布，用于建模损失中的共同冲击依赖关系，并通过马尔可夫链和随机和扩展框架，展示了其在保险索赔频率数据中的应用。

用于预测双变量非高斯时间序列的双变量广义自回归模型

Bivariate generalized autoregressive models for forecasting bivariate non-Gaussian times series

本文提出双变量广义自回归（BGAR）模型，将 VAR 推广到非正态指数族时间序列，利用条件最大似然估计并给出闭式推断工具，在巴西钩端螺旋体病数据上取得优于 GARMA、ARIMA 和 VAR 的预测效果。