Robust quickest change detection in nonstationary processes

被引：0

作者：

Hou, Yingze ^{[1
]}

Oleyaeimotlagh, Yousef ^{[1
]}

Mishra, Rahul ^{[2
]}

Bidkhori, Hoda ^{[3
]}

Banerjee, Taposh ^{[1
]}

机构：

[1] Univ Pittsburgh, Dept Ind Engn, Pittsburgh, PA 15260 USA

[2] Indian Space Res Org, UR Rao Satellite Ctr, Bangalore, India

[3] George Mason Univ, Dept Computat & Data Sci, Fairfax, VA USA

来源：

SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS | 2024年 / 43卷 / 03期

关键词：

Anomaly detection; intrusion detection; nonstationary processes; robust change detection; satellite safety;

D O I：

10.1080/07474946.2024.2356555

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Exactly and asymptotically optimal algorithms are developed for robust detection of changes in nonstationary processes. In nonstationary processes, the distribution of the data after change varies with time. The decision maker does not have access to precise information on the post-change distribution. It is shown that if the post-change, nonstationary family has a distribution that is least favorable in a well-defined sense, then the algorithms designed using the least favorable laws are robust optimal. This is the first result in which an exactly robust-optimal solution is obtained in a nonstationary setting where the least favorable law is also allowed to be nonstationary. Examples of nonstationary processes encountered in public health monitoring and space and military applications are provided. Our robust algorithms are also applied to real and simulated data to show their effectiveness.

引用

页码：275 / 300

页数：26

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