论文标题
具有时间异质性的二元纵向数据的功能聚类方法
Functional clustering methods for binary longitudinal data with temporal heterogeneity
论文作者
论文摘要
在二元纵向数据的分析中，有趣的是，对响应与协变量之间的动态关系建模是时间的函数，同时还研究了相似的时间依赖性相互作用模式。我们提出了一个新颖的广义变化模型，该模型可以说明受试者内部的可变性，并同时簇变化，而无需限制群集的数量或过度拟合数据。在对一系列二元数据的分析中，该模型提取了种群级固定效应，群集级别的变化效应和主题级随机效应。各种仿真研究表明，当群集数量未知时，提出的方法正确指定群集特异性变化的方法的有效性和实用性。所提出的方法应用于德国社会经济小组（GSOEP）研究中的一系列二元数据，在该研究中，我们确定了三个主要群集，证明了社会经济预测因子对年龄对工作状态的函数的不同影响。
In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status.
