论文标题
通过相关模型选择，将双变量分布的一般家族应用于建模相关的竞争风险数据
Application of a General Family of Bivariate Distributions in Modelling Dependent Competing Risks Data with Associated Model Selection
论文作者
论文摘要
在本文中，使用双变量分布的一般家族用于对竞争风险数据进行与因子相关因素进行建模。此处考虑的竞争风险数据的一般结构包括联系。提出了针对拟议模型的全面推论框架：在给定依赖竞争风险数据的双变量分布家族中，最大似然估计，置信区间构建和模型选择。推论方法非常方便实现。通过详细的模拟，观察到推论方法可提供相当合理的结果。从建议的模型的帮助下，对糖尿病性视网膜病研究的真实数据进行分析。
In this article, a general family of bivariate distributions is used to model competing risks data with dependent factors. The general structure of competing risks data considered here includes ties. A comprehensive inferential framework for the proposed model is presented: maximum likelihood estimation, confidence interval construction, and model selection within the bivariate family of distributions for a given dependent competing risks data. The inferential methods are very convenient to implement. Through detailed simulations, the inferential methods are observed to provide quite reasonable results. Analysis of a real data from the Diabetic Retinopathy Study is carried out with the help of the proposed model as an illustrative example.
