Random Effects Modelling and Inference for Skewed Data of Complex Correlation Structures

在竞争性群落中，在同一地点观察到的不同种类的植物或动物的频率通常是负相关的，而居住在同一群落中的人们的生活经历往往是正相关的，因为共享的环境和其他潜在的群落特征。随着时间和空间的相关变化的调查是许多环境和健康研究的核心部分。我的研究兴趣在于开发新的随机效应模型，其中随机效应可用于解释社区效应和邻近社区之间的关联。我们的模型特别是解释与合理的推论。该方法最大的优点是对具有复杂依赖结构的海量数据具有较高的计算效率。我们的研究项目正在进行的应用包括乙二醇对出生体重和畸形的影响在毒理学研究，交通事故的时空分布在芝加哥，汞检测在千鱼从数百个湖泊在安大略，空气污染和死亡率在美国，地震伤亡预测在中国和加拿大的森林火灾。我们的研究结果将有助于政策制定者在地理上有针对性地分配资源，并进行区域规划，以试图纠正问题领域。

Observed frequencies for different species of plants or animals at the same location are usually negatively correlated in a competitive community, whereas the life experiences of people residing in the same community tend to be positively correlated because of shared environment and other underlying community characteristics. The investigation of relevant changes over time and space is a central part of many environmental and health studies. My research interests lie in the development of new random effects models where random effects can be used to account for community effects and the association among neighbouring communities. Our models are especially interpretable with well justified inferences. The most distinctive advantage of our method is its computational efficiency for massive data of complex dependence structure. Ongoing applications of our research projects include effects of ethylene glycol on birth weight and malformation in toxicological studies, spatial and temporal distribution of traffic accidents in Chicago, mercury detection in thousand fish from hundred lakes in Ontario, air pollution and mortality across USA, earthquake casualty prediction in China and forest fires across Canada. Our findings will help policy makers geographically target resource allocation and make regional planning in an attempt to remediate problem areas.