Text

Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control

In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this article, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev–Roberts–Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.65 No.1 February 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 33-43

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CHANGE-POINT DETECTION
THOMPSON SAMPLING
ADAPTIVE SAMPLING
PARTIALLY OBERVED VARIABLES
SHIRYAIEV-ROBERTS PROCEDURE

Info Detail Spesifik

https://doi.org/10.1080/00401706.2022.2054861

Pernyataan Tanggungjawab

Wanrong Zhang, Yajun Mei

Versi lain/terkait

Lampiran Berkas

Komentar