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学术报告七十:Consistent selection of the number of change-points

来源:数学与统计学院     作者:     时间:2018/7/13 17:36:26  0次

数学与统计学院学术报告[2018] 070

(高水平大学建设系列报告189)

报告题目: Consistent selection of the number of change-points via sample-splitting

报告人: 邹长亮  教授(南开大学

报告时间:71515:00-16:00

报告地点: 科技楼501                        

报告内容:

In multiple change-point analysis, one of the major challenges is to estimate

the number of change-points. Most existing approaches attempt to minimize a

Schwarz information criterion which balances a term quantifying model fit with

a penalization term accounting for model complexity that increases with the

number of change-points and limits overfitting. However, different penalization

terms are required to adapt to different contexts of multiple change-point

problems and the optimal penalization magnitude usually varies from the model

and error distribution.  We propose a data-driven selection criterion that is

applicable to most kinds of popular change-point detection methods, including

binary segmentation and optimal partitioning algorithms. The key idea is to

select the number of change-points that minimizes the squared prediction error,

which measures the fit of a specified model for a new sample. We develop a

cross-validation estimation scheme based on an order-preserved sample-splitting

strategy, and establish its asymptotic selection consistency under some mild

conditions. The proposed selection criterions effectiveness is demonstrated on

a variety of numerical experiments and real-data examples.

报告人简历:

邹长亮,南开大学统计研究院教授。主要研究方向包括统计过程控制、变点理论、高维数据分析等。2008年于南开大学博士学位;2013年破格晋升教授。入选国家“万人计划”青年拔尖人才,教育部“青年长江学者”,获国家优秀青年科学基金支持。曾获“全国百篇优秀博士论文”,“钟家庆数学奖”,天津市自然科学一等奖和霍英东教育基金会高校青年教师一等奖等。入选2015爱思唯尔(Elsevier)“中国高被引学者”,是国际统计学会(ISI)推选会员,并任TechnometricsJQT等国际统计著名杂志的AE

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