A binning approach to quickest change detection with unknown post-change distribution

The problem of quickest detection of a change in distribution is considered under the assumption that the prechange distribution is known, and the postchange distribution is only known to belong to a family of distributions distinguishable from a discretized version of the prechange distribution. A...

Full description

Saved in:
Bibliographic Details
Main Authors: Lau, Tze Siong, Tay, Wee Peng, Veeravalli, Venugopal V.
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/137146
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-137146
record_format dspace
spelling sg-ntu-dr.10356-1371462020-03-02T07:03:23Z A binning approach to quickest change detection with unknown post-change distribution Lau, Tze Siong Tay, Wee Peng Veeravalli, Venugopal V. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Quickest Change Detection Non-parametric Test The problem of quickest detection of a change in distribution is considered under the assumption that the prechange distribution is known, and the postchange distribution is only known to belong to a family of distributions distinguishable from a discretized version of the prechange distribution. A sequential change detection procedure is proposed that partitions the sample space into a finite number of bins and monitors the number of samples falling into each of these bins to detect the change. A test statistic that approximates the generalized likelihood ratio test is developed. It is shown that the proposed test statistic can be efficiently computed using a recursive update scheme, and a procedure for choosing the number of bins in the scheme is provided. Various asymptotic properties of the test statistic are derived to offer insights into its performance tradeoff between average detection delay and average run length to false alarm. Testing on synthetic and real data demonstrates that our approach is comparable or better in the performance to existing nonparametric change detection methods. MOE (Min. of Education, S’pore) Accepted version 2020-03-02T07:03:22Z 2020-03-02T07:03:22Z 2018 Journal Article Lau, T. S., Tay, W. P., & Veeravalli, V. V. (2019). A binning approach to quickest change detection with unknown post-change distribution. IEEE Transactions on Signal Processing, 67(3), 609-621. doi:10.1109/TSP.2018.2881666 1053-587X https://hdl.handle.net/10356/137146 10.1109/TSP.2018.2881666 2-s2.0-85056739268 3 67 609 621 en IEEE Transactions on Signal Processing © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSP.2018.2881666. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Quickest Change Detection
Non-parametric Test
spellingShingle Engineering::Electrical and electronic engineering
Quickest Change Detection
Non-parametric Test
Lau, Tze Siong
Tay, Wee Peng
Veeravalli, Venugopal V.
A binning approach to quickest change detection with unknown post-change distribution
description The problem of quickest detection of a change in distribution is considered under the assumption that the prechange distribution is known, and the postchange distribution is only known to belong to a family of distributions distinguishable from a discretized version of the prechange distribution. A sequential change detection procedure is proposed that partitions the sample space into a finite number of bins and monitors the number of samples falling into each of these bins to detect the change. A test statistic that approximates the generalized likelihood ratio test is developed. It is shown that the proposed test statistic can be efficiently computed using a recursive update scheme, and a procedure for choosing the number of bins in the scheme is provided. Various asymptotic properties of the test statistic are derived to offer insights into its performance tradeoff between average detection delay and average run length to false alarm. Testing on synthetic and real data demonstrates that our approach is comparable or better in the performance to existing nonparametric change detection methods.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Lau, Tze Siong
Tay, Wee Peng
Veeravalli, Venugopal V.
format Article
author Lau, Tze Siong
Tay, Wee Peng
Veeravalli, Venugopal V.
author_sort Lau, Tze Siong
title A binning approach to quickest change detection with unknown post-change distribution
title_short A binning approach to quickest change detection with unknown post-change distribution
title_full A binning approach to quickest change detection with unknown post-change distribution
title_fullStr A binning approach to quickest change detection with unknown post-change distribution
title_full_unstemmed A binning approach to quickest change detection with unknown post-change distribution
title_sort binning approach to quickest change detection with unknown post-change distribution
publishDate 2020
url https://hdl.handle.net/10356/137146
_version_ 1681041597564715008