报告题目：A Bivariate Bayesian Method for Interval-Valued Regression Models
报告时间：2021年11月18日(周四) 15:00—16:00
报告地点: 线上
腾讯会议ID：751 973 762
报告人：秦中峰教授
报告人简介：
秦中峰，北京航空航天大学经济管理学院教授、博士生导师。2009年毕业于清华大学，获博士学位，曾赴香港城市大学、新加坡南洋理工大学交流，美国密歇根大学访问学者。主要从事数据分析与管理决策的交叉研究，在国内外高水平学术期刊发表论文50余篇，出版学术专著1部，先后主持省部级以上科研项目近10项。2012年入选教育部新世纪优秀人才支持计划，先后获得教育部霍英东青年教师奖、钟家庆运筹学奖等，目前担任中国系统工程学会应急管理专委会秘书长、中国运筹学会智能计算分会副理事长、北京大数据协会常务理事等。
报告摘要：
As typical symbolic data, interval-valued data offer a useful tool to handle massive datasets. There has been a lot of literature focusing on researching regression models for interval-valued data based on the center and range method (CRM). However, few works are devoted to exploring Bayesian methods for interval-valued data. In this paper, we extend CRM for interval-valued regression models to the Bayesian framework for the first time. We propose a bivariate Bayesian regression model based on CRM with known and unknown covariance matrices, respectively. The experimental results of synthetic and real datasets show that, in contrast with classical models, the proposed Bayesian model has advantages in forecasting performances.
