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Modelling spatially correlated binary data
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报告主题:Modelling spatially correlated binary data

报告人:潘建新,教授,英国曼彻斯特大学

照片:

邀请人:刘三阳 教授

报告时间:2019年08月19日(周一)下午16:00-17:00

报告地点:信远楼II205数统院会议室

报告人简介:潘建新教授,英国曼彻斯特大学(University of Manchester)数学学院终身教授,英国皇家统计学会(The Royal Statistical Society) 会士(Fellow), 国际统计学会(International Statistical Institute)当选会员(elected member)和美国数理统计学会(Institute of Mathematical Statistics)会员。统计学杂志Biometrics(2008-2018), Journal of Multivariate Analysis (2018-)和Biometrical Journal (2016-)编委(Associated Editor)。1996年在香港浸会大学获得统计学博士学位,之后到英国洛桑(Rothamsted)实验中心从事博士后研究。2002年10月加盟曼彻斯特大学数学学院,先后仼讲师(2002)、高级讲师(2004)、Reader(2005)。2006年被曼彻斯特大学聘为终身教授,并兼任曼彻斯特大学医学院研究员。曾担任曼大数学学院概率统计系系主任。致力于统计学领域内复杂数据模型的理论研究及其在医学、金融及工业上的应用,取得了多项创新性研究成果。成果发表在包括Journal of the American Statistical Association和Biometrika在内的统计学主流期刊上。至今已发表学术论文100余篇,出版学术专著2部(Growth Curve Models and Statistical Diagnostics和Case-Deletion Diagnostics in Linear Mixed Models),其中1部于2002年由Springer出版社出版。已指导18名博士研究生并获得学位。

报告摘要: Generalized estimating equations lead to consistent estimators of the mean parameters for spatially correlated binary data, but the estimation of covariance matrix is also of interest in spatial data analysis. In this talk, a specific parametric form is proposed to model the correlation matrix for spatially correlated binary data. An iterative approach based on generalized estimating equations is developed to estimate the mean and correlation parameters simultaneously. Asymptotic normality for the estimators of the mean and correlation parameters is provided. Simulation studies are conducted through considering various model parameters such as different working correlation matrices, correlation parameters and dimensions of the mean parameters. The proposed approach is used to analyze the spatial bovine tuberculosis infection data in Ireland, aiming to quantify the influence of some important factors on the infection for both badgers and cattle, as well as the correlation between their setts and herds.

Joint work with Youjun Huang of Sichuan University, China and Gabrielle Kelly of University College of Dublin, Ireland.

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