de Freitas João Victor B, Nobre Juvêncio S, Bourguignon Marcelo, Santos-Neto Manoel
Departamento de Estatística, Instituto de Matemática, Estatística e Computação Científica, Universidade Estadual de Campinas, Campinas, Brazil.
Departamento de Estatística e Matemática Aplicada, Universidade Federal do Ceará, Fortaleza, Brazil.
J Appl Stat. 2021 Aug 10;49(15):3784-3803. doi: 10.1080/02664763.2021.1963422. eCollection 2022.
In many situations, it is common to have more than one observation per experimental unit, thus generating the experiments with repeated measures. In the modeling of such experiments, it is necessary to consider and model the intra-unit dependency structure. In the literature, there are several proposals to model positive continuous data with repeated measures. In this paper, we propose one more with the generalization of the beta prime regression model. We consider the possibility of dependence between observations of the same unit. Residuals and diagnostic tools also are discussed. To evaluate the finite-sample performance of the estimators, using different correlation matrices and distributions, we conducted a Monte Carlo simulation study. The methodology proposed is illustrated with an analysis of a real data set. Finally, we create an package for easy access to publicly available the methodology described in this paper.
在许多情况下，每个实验单元有多个观测值是很常见的，从而产生了重复测量实验。在对此类实验进行建模时，有必要考虑并对单元内的依赖结构进行建模。在文献中，有几种对具有重复测量的正连续数据进行建模的方法。在本文中，我们通过推广贝塔素数回归模型又提出了一种方法。我们考虑了同一单元观测值之间存在依赖关系的可能性。还讨论了残差和诊断工具。为了评估估计量在不同相关矩阵和分布下的有限样本性能，我们进行了蒙特卡罗模拟研究。通过对一个实际数据集的分析来说明所提出的方法。最后，我们创建了一个软件包，以便于轻松获取本文所述的公开可用方法。
