Robust Quickest Change Detection in Statistically Periodic Processes

被引：0

作者：

Banerjee, Taposh ^{[1
]}

Taha, Ahmad ^{[1
]}

John, Eugene ^{[1
]}

机构：

[1] Univ Texas San Antonio, San Antonio, TX 78249 USA

来源：

2021 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT) | 2021年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/ISIT45174.2021.9518067

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The problem of detecting a change in the distribution of a statistically periodic process is investigated. The problem is posed in the framework of independent and periodically identically distributed (i.p.i.d.) processes, a recently introduced class of processes to model statistically periodic data. An algorithm is proposed that is shown to be robust against an uncertainty in the post-change law. The motivation for the problem comes from event detection problems in traffic data, social network data, electrocardiogram data, and neural data, where periodic statistical behavior has been observed.

引用

页码：101 / 105

页数：5

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