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主    题： Quasi-Likelihood for Spatial Point Processes

主讲人：官永涛教授（迈阿密大学工商管理学院管理科学系系主任, 教授）

时    间：2015年7月10日（周五）下午15:00-16:00

地   点：北院卓远楼307

主办单位：统计与数学学院

摘要：Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geographical or environmental covariates. When Cox or cluster process models are used to accommodate clustering not accounted for by the available covariates, likelihood based inference becomes computationally cumbersome due to the complicated nature of the likelihood function and the associated score function. It is therefore of interest to consider alternative more easily computable estimating functions. We derive the optimal estimating function in a class of first-order estimating functions. The optimal estimating function depends on the solution of a certain Fredholm integral equation which in practice is solved numerically. The derivation of the optimal estimating function has close similarities to the derivation of quasi-likelihood for standard data sets. The approximate solution is further equivalent to a quasi-likelihood score for binary spatial data. We therefore use the term quasi-likelihood for our optimal estimating function approach. We demonstrate in a simulation study and a data example that our quasi-likelihood method for spatial point processes is both statistically and computationally efficient.

官永涛教授简介：统计学博士(Texas A & M University), 迈阿密大学工商管理学院管理科学系系主任, 教授, 美国统计协会（ASA）fellow, 担任期刊Journal of the American Statistical Association的Associate Editor, 主要研究领域为: Point processes, spatial-temporal processes, spatial epidemiology, longitudinal data analysis等, 在Journal of the American Statistical Association, Journal of the Royal Statistical Society, Series B, Biometrika等统计学顶级期刊上发表论文数十篇.