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Statistical Clustering of Semiparametric Dynamic Networks through Stochastic Snapshots

发布时间:2020-12-14 作者: 浏览次数:
Speaker: 赵晓兵 DateTime: 2020年12月15日(周二)下午19:00-20:00
Brief Introduction to Speaker:

赵晓兵,浙江财经大学数据科学学院,教授,博士生导师;2006年于香港理工大学获得博士学位,先后在澳大利亚麦考瑞大学、美国西北大学等高校进行交流访问;在统计学国际主流SCI期刊发表论文三四十篇,主持国家自然科学基金和社科基金多项,获浙江省高校中青年科学带头人等荣誉称号。研究兴趣广泛,包括生存分析、复发事件分析,高维数据分析,网络数据等。

Place: 腾讯会议(会议号请联系左国新老师索取)
Abstract:The analysis of dynamic network data based on statistical models has attracted wide attention in social and biological research fields, where the interactions between individuals may undertake large and systematic changes. In this paper, we propose a statistical model for the recurrent events of instantaneous interactions between the nodes, in which a Poisson process with a semiparametric mean function of recurrent interactions is considered under the condition of latent membership of the nodes. A joint model of the recurrent interaction process and discrete-time observation process is proposed to characterize the impact of the time-slices for the snapshots. A variational expectation-maximization algorithm is applied to obtain the estimators of the connectivity parameters and the latent variables. The asymptotic properties of the estimates are also discussed.