统计学相关讲座

Estimating Social Intercorrelation with Sampled Network Data个人简介

被阅览数：次  发布时间:2014/03/18 11:52:43

主讲人： Prof. Hansheng Wang
主讲人简介： Guanghua School of Management, Peking University
简介：
Social intercorrelation is a parameter of importance for network data analysis. To estimate social intercorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc). In that case, one has to rely on sampled network data to infer about social intercorrelation. By doing so, network relationships (i.e., edges) involving unsampled nodes are overlooked. This leads to distorted network structure and underestimated social intercorrelation. To solve the problem, we propose here a novel solution. It makes use of the fact that social intercorrelation is typically small. This enables us to approximate the targeting likelihood by its first order Taylor's expansion. Depending on the choice of the likelihood, we obtain respectively an approximate maximum likelihood estimator (AMLE) and paired maximum likelihood estimator (PMLE). We show theoretically that both methods are consistent and asymptotically normal with identical asymptotic efficiency. However, the difference is that PMLE is computationally superior. Numerical studies based on both simulated and real datasets are presented for illustration purpose.
时间： 16:30-17:30, Thursday, March 20, 2014
地点： Room N302, Economics Building
期数： 厦门大学统计学高级系列讲座
主办单位：
类型： 系列讲座

上一条：Double Asymptotics for Explosive Continuous Time Models个人简介
下一条：宏观经济形势分析论坛个人简介