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讲座:Variance Change Point Detection Under a Smoothly-Changing Mean Trend with Application to Liver Procurement

发布者:管理科学系    发布时间:2021-09-23

题 目:Variance Change Point Detection Under a Smoothly-Changing Mean Trend with Application to Liver Procurement

嘉 宾高振国 副教授 上海交通大学数学科学学院

主持人:何滔滔 助理教授  上海交通大学安泰经济与管理学院

时 间:2021年09月29日(周三)10:00-11:30

地 点:安泰楼A303

内容简介:

Literature on change point analysis mostly requires a sudden change in the data distribution, either in a few parameters or the distribution as a whole. We are interested in the scenario, where the variance of data may make a significant jump while the mean changes in a smooth fashion. The motivation is a liver procurement experiment monitoring organ surface temperature. Blindly applying the existing methods to the example can yield erroneous change point estimates since the smoothly changing mean violates the sudden-change assumption. We propose a penalized weighted least-squares approach with an iterative estimation procedure that integrates variance change point detection and smooth mean function estimation. The procedure starts with a consistent initial mean estimate ignoring the variance heterogeneity. Given the variance components the mean function is estimated by smoothing splines as the minimizer of the penalized weighted least squares. Given the mean function, we propose a likelihood ratio test statistic for identifying the variance change point. The null distribution of the test statistic is derived together with the rates of convergence of all the parameter estimates. Simulations show excellent performance of the proposed method. Application analysis offers numerical support to non-invasive organ viability assessment by surface temperature monitoring. Supplementary materials for this article are available online.

演讲人简介

高振国,上海交通大学数学科学学院统计系长聘教轨副教授,博士生导师。2018年博士毕业于弗吉尼亚理工大学统计学系。主要科研方向包括数理统计、变点检测以及异常变化检测、大数据分析、数据挖掘及机器学习、非参模型、高维数据分析等。曾在美国制药公司高级统计分析师,负责药物研发的数据分析、统计建模等研究工作。

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