TY - GEN
T1 - Quickest Detection in High-Dimensional Linear Regression Models via Implicit Regularization
AU - Xu, Qunzhi
AU - Yu, Yi
AU - Mei, Yajun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In this paper, we consider the quickest detection problem in high-dimensional streaming data, where the unknown regression coefficients might change at some unknown time. We propose a quickest detection algorithm based on the implicit regularization algorithm via gradient descent, and provide theoretical guarantees on the average run length to false alarm and detection delay. Numerical studies are conducted to validate the theoretical results.
AB - In this paper, we consider the quickest detection problem in high-dimensional streaming data, where the unknown regression coefficients might change at some unknown time. We propose a quickest detection algorithm based on the implicit regularization algorithm via gradient descent, and provide theoretical guarantees on the average run length to false alarm and detection delay. Numerical studies are conducted to validate the theoretical results.
UR - http://www.scopus.com/inward/record.url?scp=85202837073&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85202837073&partnerID=8YFLogxK
U2 - 10.1109/ISIT57864.2024.10619577
DO - 10.1109/ISIT57864.2024.10619577
M3 - Conference contribution
AN - SCOPUS:85202837073
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1059
EP - 1064
BT - 2024 IEEE International Symposium on Information Theory, ISIT 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Symposium on Information Theory, ISIT 2024
Y2 - 7 July 2024 through 12 July 2024
ER -