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

缺失数据的零膨胀随机效应模型的统计推断

• 批准号: RGPIN-2016-04322
• 负责人: Yan, Guohua
• 金额: $ 1.09万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635939
• 关键词: Statistical; inference; zero; inflated; random

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.

相关数据在生物学、生态学、健康和医学研究等各种学科中很常见。数据是相关的，例如，重复测量，或巢设计，或单位的自然层次结构。随机效应模型（或混合模型或分层模型）被广泛用于分析这些数据，其中引入随机效应以解释层次内的相关性。例如，在医学纵向研究中，随机效应通常用于模拟受试者之间的异质性。这些随机效应表征了受试者对某些疾病的不同易感性。