羊城讲坛第十一讲——中国人民大学周静

Note on Estimating Network Dependence in a Discrete Choice Model

2017年11月14日，星期二上午10:00在大学城771771威尼斯.cm大全行政东楼前座412会议室。中国人民大学统计学院周静助理教授为各位学子带来了精彩的学术讲座--《Note on Estimating Network Dependence in a Discrete Choice Model》。

报告人简介：

周静，中国人民大学统计学院助理教授，北京大学光华管理学院管理学博士，研究上关注复杂网络数据建模、营销模型、消费者行为分析等，研究论文发表于Journal of business and economic Statistics、Science China Mathematics、Statistics and its Interface,

StatisticaSinica、管理科学、营销科学学报等国内外权威杂志上。在产业实践上，对客户流失预警模型、销售预测模型、用户欺诈模型等相关模型具有丰富的实战经验。热衷案例创作，是微信公众号狗熊会精品案例文本分析系列的案例组长。

摘要:

Discrete choice model is probably one of the most popularly used statistical methodsin practice. The common feature of this model is that it considers the behavioral factors of a person and the assumption of independent individuals. However, this widely accepted assumption seems problematic because human beings do not live in isolation. They interact with each other and form complex networks. Then the application of discrete choice model to network data willallow for network dependence in a general framework. In this paper, we focus on a discrete choice model with probit error which is specified as a latent spatial autoregressive model(SAR). This model could be viewed as a natural extension of the classical SAR model. The key difference is that thenetwork dependence is latent and unobservable. Instead, it could be measured by a binary response variable. Parameter estimation then becomes a challenging task due to thecomplicated objective function. Following the idea of composite likelihood, an approximated paired maximum likelihood estimator (APMLE) is developed. Numerical studiesare carried out to assess the finite sample performance of the proposed estimator. Finally,a real dataset of Sina Weibo isanalyzed for illustration purpose.