【12月16日】商学院学术论坛：Estimating Social Inter-correlation with Sampled Network Data发布日期：2019-09-12 21:10:04
主讲题目：Estimating Social Inter-correlation with Sampled Network Data
主讲人：周静 北京大学光华管理学院 博士
Social inter-correlation is a parameter of importance for understanding consumers’ relationship. To estimate social inter-correlation, 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 inter-correlation. By doing so, network relationships (i.e., edges) involving un-sampled nodes are overlooked. This leads to distorted network structure and underestimated social inter-correlation. To solve the problem, we propose here a novel solution. It makes use of the fact that social inter-correlation 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.
北京大学光华管理学院市场营销系博士研究生，2012年毕业于中央财经大学商学院，获管理学学士学位，同年保送为北大光华直博生。博士期间的主要研究方向为社会化网络营销，移动互联网营销以及消费者行为数据的高效计算。研究成果发表在国际TOP期刊Journal of Business and Economic Statistics和国内TOP营销期刊《营销科学学报》等杂志上。