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

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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.