主 題：Prediction Models for Network-linked Data
主講人：朱冀美國密歇根大學統(tǒng)計系教授
主持人：石磊統(tǒng)計與數學學院院長
時 間：2016年12月26日（周一）上午10:00-11:00
地 點：北院卓遠樓305
主辦單位：統(tǒng)計與數學學院
摘 要：Prediction problems typically assume the training data areindependent samples, but in many modern applications samples come fromindividuals connected by a network. For example, in adolescent healthstudies of risk-taking behaviors, information on the subjects' socialnetworks is often available and plays an important role throughnetwork cohesion, the empirically observed phenomenon of friendsbehaving similarly. Taking cohesion into account in prediction modelsshould allow us to improve their performance. Here we propose aregression model with a network-based penalty on individual nodeeffects to encourage similarity between predictions for linked nodes,and show that it performs better than traditional models boththeoretically and empirically when network cohesion is present. Theframework is easily extended to other models, such as the generalizedlinear model and Cox's proportional hazard model. Applications topredicting levels of recreational activity and marijuana usage amongteenagers based on both demographic covariates and their friendship
networks are discussed in detail and demonstrate the effectiveness ofour approach.
朱冀教授簡介：美國斯坦福大學統(tǒng)計學博士,，美國密歇根大學統(tǒng)計系教授,，研究領域為統(tǒng)計機器學習與數據挖掘、研究興趣包括高維數據分析,、網絡數據分析等,，在國際主流學術刊物上共發(fā)表70 多篇學術論文,，擔任包括國際統(tǒng)計學頂尖刊物《Journal of the American Statistical Association》、《Biometrika》在內的多個期刊副主編,。
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