Statistical inference in zero-inflated random effects models with missing data

点击翻译按钮获取中文摘要

Correlated data are common in a variety of disciplines such as biological, ecological, health and medical studies. Data are correlated due to, for example, repeated-measures, or a nest design, or a natural hierarchy of units. Random effects models (or mixed models or hierarchical models) are widely used to analyse these data, in which random effects are introduced to account for correlation within a hierarchy. For example, in medical longitudinal studies, random effects are often used to model the heterogeneity between subjects. These random effects characterize the varying susceptibilities of subjects to certain diseases.