NDLI: Asymptotically Optimal Truncated Multivariate Gaussian Hypothesis Testing With Application to Consensus Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Jiangfan Zhang Blum, R.S.
Copyright Year	1991
Abstract	In the interest of complexity reduction or to facilitate efficient distributed computation using consensus, truncated versions of the optimal hypothesis test are considered for a canonical multivariate Gaussian problem with L observations. The truncated tests employ correlation terms involving any given observation. The focus is on cases with a large L such that significant efficiency results with a truncation rule, k as a function of L, which increases very slowly with L. A key result provides sufficient conditions on truncation rules and sequences of hypothesis testing problems which provide no loss in deflection performance as L approaches infinity when compared to the optimal detector. The set of asymptotically optimal truncation rules satisfying these sufficient conditions varies with the scaling behavior of the difficulty of the hypothesis testing problem with L. Several popular classes of system and process models, including observations from wide-sense stationary limiting processes as L→∞ after the mean is subtracted, are used as illustrative classes of examples to demonstrate the sufficient conditions are not overly restrictive. In these examples, significant truncation can be employed even when the difficulty of the hypothesis testing problem scales in the least favorable manner, putting the most stringent conditions on the truncation rule. In all the cases considered, numerical results imply the fixed-false-alarm-rate detection probability of the truncated detector converges to the detection probability of the optimal detector for our asymptotically optimal truncation in terms of deflection.
Sponsorship	IEEE Signal Processing Society
Starting Page	431
Ending Page	442
Page Count	12
File Size	3971573
File Format	PDF
ISSN	1053587X
Volume Number	62
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Testing Signal processing algorithms Vectors Error probability Complexity theory Limiting truncated detector Consensus deflection large system multivariate Gaussian hypothesis test sensor network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Asymptotically optimal truncated hypothesis test for a large sensor network described by a multivariate Gaussian distribution

Asymptotically Optimal Anomaly Detection via Sequential Testing

Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis

Design and performance analysis of parametric suboptimal detectors in SαS noise

An asymptotically optimal detector for Gaussianity testing

2011 ieee statistical signal processing workshop (ssp) multilevel minimax hypothesis testing.

Performance comparison between matched filter and locally optimal detector for composite hypothesis test with inaccurate noise

Large Deviations Performance of Consensus+Innovations Distributed Detection With Non-Gaussian Observations

Asymptotically Uniformly Minimax Detection and Isolation in Network Monitoring

Asymptotically Optimal Truncated Multivariate Gaussian Hypothesis Testing With Application to Consensus Algorithms

Similar Documents

Asymptotically optimal truncated hypothesis test for a large sensor network described by a multivariate Gaussian distribution

Asymptotically Optimal Anomaly Detection via Sequential Testing

Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis

Design and performance analysis of parametric suboptimal detectors in SαS noise

An asymptotically optimal detector for Gaussianity testing

2011 ieee statistical signal processing workshop (ssp) multilevel minimax hypothesis testing.

Performance comparison between matched filter and locally optimal detector for composite hypothesis test with inaccurate noise

Large Deviations Performance of Consensus+Innovations Distributed Detection With Non-Gaussian Observations

Asymptotically Uniformly Minimax Detection and Isolation in Network Monitoring

Asymptotically Optimal Truncated Multivariate Gaussian Hypothesis Testing With Application to Consensus Algorithms