论文标题
依赖数据的灵活边缘模型
Flexible Marginal Models for Dependent Data
论文作者
论文摘要
依赖数据的模型由其推理目标区分。当兴趣在于量化集群群体的平均关联时，边际模型就很有用。当未知协变量结合的功能形式时，需要柔性回归方法来允许潜在的非线性关系。我们提出了一种新型的边缘添加剂模型（MAM），用于与非线性人群平均关联建模与集群相关的数据。提出的MAM是一个统一的框架，用于估算边缘平均模型的不确定性定量，结合推断群间变异性和集群特异性预测。我们提出了一种拟合算法，该算法能够有效地计算标准错误，并纠正惩罚条款。我们证明了在模拟和（i）对海狸觅食行为的纵向研究中提出的方法，以及（ii）西非Loaloa感染的空间分析。用于实施该方法的R代码可在https://github.com/awstringer1/mam上获得。
Models for dependent data are distinguished by their targets of inference. Marginal models are useful when interest lies in quantifying associations averaged across a population of clusters. When the functional form of a covariate-outcome association is unknown, flexible regression methods are needed to allow for potentially non-linear relationships. We propose a novel marginal additive model (MAM) for modelling cluster-correlated data with non-linear population-averaged associations. The proposed MAM is a unified framework for estimation and uncertainty quantification of a marginal mean model, combined with inference for between-cluster variability and cluster-specific prediction. We propose a fitting algorithm that enables efficient computation of standard errors and corrects for estimation of penalty terms. We demonstrate the proposed methods in simulations and in application to (i) a longitudinal study of beaver foraging behaviour, and (ii) a spatial analysis of Loaloa infection in West Africa. R code for implementing the proposed methodology is available at https://github.com/awstringer1/mam.
