ABSTRACT

Assessing the temporal dependency among outcomes under investigation is critical in many fields. One complication in the modeling process of the discrete longitudinal data is the presence of excess zeros. We propose a framework for modeling count repeated measurements using members of power series family of distributions. The framework accommodates count outcomes having extra zeros. The longitudinal observations of response variable is modeled using pair copula constructions with a D-vine structure. The maximum likelihood estimates of parameters are obtained using a two-stage approach. Some simulation studies are performed for illustration of the proposed methods, for comparing its performance with that of a generalized linear mixed effects (GLME) model and for assessing the robustness of D-vine and GLME models with respect to the distribution of random effects. In the empirical analysis, the proposed method is applied for analysing a real data set of a kidney allograft rejection study.

摘要

评估调查结果之间的时间依赖性在许多领域都至关重要。离散纵向数据建模过程中的一个复杂问题是存在多余的零点。我们提出了一个使用幂级数分布族成员对计数重复测量进行建模的框架。该框架适应具有额外零的计数结果。响应变量的纵向观察是使用具有 D-vine 结构的对联结结构建模的。使用两阶段方法获得参数的最大似然估计。进行了一些模拟研究以说明所提出的方法，用于将其性能与广义线性混合效应 (GLME) 模型的性能进行比较，并评估 D-vine 和 GLME 模型在随机效应分布方面的稳健性。在实证分析中，所提出的方法用于分析肾脏同种异体移植排斥研究的真实数据集。