Opportunistic detection rules

Wenyi Zhang; George V. Moustakides; H. Vincent Poor

doi:10.1109/ISIT.2014.6874932

Opportunistic detection rules

Wenyi Zhang, George V. Moustakides, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Opportunistic detection rules (ODRs) are variants of fixed-sample-size detection rules in which the statistician is allowed to make an early decision on the alternative hypothesis opportunistically based on the sequentially observed samples. From a sequential decision perspective, ODRs are also mixtures of one-sided and truncated sequential detection rules. Several key properties of ODRs are established in this paper, in both the asymptotic regime in which the maximum sample size grows without bound, and the finite regime in which the maximum samples size is a fixed finite number. Furthermore, an extended setup, in which the maximum sample size is a random variable following a geometric distribution whose realization is not revealed to the statistician until observing the last sample, is studied.

Original language	English (US)
Title of host publication	2014 IEEE International Symposium on Information Theory, ISIT 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	746-750
Number of pages	5
ISBN (Print)	9781479951864
DOIs	https://doi.org/10.1109/ISIT.2014.6874932
State	Published - 2014
Event	2014 IEEE International Symposium on Information Theory, ISIT 2014 - Honolulu, HI, United States Duration: Jun 29 2014 → Jul 4 2014

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
ISSN (Print)	2157-8095

Other

Other	2014 IEEE International Symposium on Information Theory, ISIT 2014
Country/Territory	United States
City	Honolulu, HI
Period	6/29/14 → 7/4/14

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.2014.6874932

Cite this

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title = "Opportunistic detection rules",

abstract = "Opportunistic detection rules (ODRs) are variants of fixed-sample-size detection rules in which the statistician is allowed to make an early decision on the alternative hypothesis opportunistically based on the sequentially observed samples. From a sequential decision perspective, ODRs are also mixtures of one-sided and truncated sequential detection rules. Several key properties of ODRs are established in this paper, in both the asymptotic regime in which the maximum sample size grows without bound, and the finite regime in which the maximum samples size is a fixed finite number. Furthermore, an extended setup, in which the maximum sample size is a random variable following a geometric distribution whose realization is not revealed to the statistician until observing the last sample, is studied.",

author = "Wenyi Zhang and Moustakides, {George V.} and Poor, {H. Vincent}",

year = "2014",

doi = "10.1109/ISIT.2014.6874932",

language = "English (US)",

isbn = "9781479951864",

series = "IEEE International Symposium on Information Theory - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "746--750",

booktitle = "2014 IEEE International Symposium on Information Theory, ISIT 2014",

address = "United States",

note = "2014 IEEE International Symposium on Information Theory, ISIT 2014 ; Conference date: 29-06-2014 Through 04-07-2014",

}

Zhang, W, Moustakides, GV & Poor, HV 2014, Opportunistic detection rules. in 2014 IEEE International Symposium on Information Theory, ISIT 2014., 6874932, IEEE International Symposium on Information Theory - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 746-750, 2014 IEEE International Symposium on Information Theory, ISIT 2014, Honolulu, HI, United States, 6/29/14. https://doi.org/10.1109/ISIT.2014.6874932

Opportunistic detection rules. / Zhang, Wenyi; Moustakides, George V.; Poor, H. Vincent.
2014 IEEE International Symposium on Information Theory, ISIT 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 746-750 6874932 (IEEE International Symposium on Information Theory - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Zhang, Wenyi

AU - Moustakides, George V.

AU - Poor, H. Vincent

PY - 2014

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N2 - Opportunistic detection rules (ODRs) are variants of fixed-sample-size detection rules in which the statistician is allowed to make an early decision on the alternative hypothesis opportunistically based on the sequentially observed samples. From a sequential decision perspective, ODRs are also mixtures of one-sided and truncated sequential detection rules. Several key properties of ODRs are established in this paper, in both the asymptotic regime in which the maximum sample size grows without bound, and the finite regime in which the maximum samples size is a fixed finite number. Furthermore, an extended setup, in which the maximum sample size is a random variable following a geometric distribution whose realization is not revealed to the statistician until observing the last sample, is studied.

AB - Opportunistic detection rules (ODRs) are variants of fixed-sample-size detection rules in which the statistician is allowed to make an early decision on the alternative hypothesis opportunistically based on the sequentially observed samples. From a sequential decision perspective, ODRs are also mixtures of one-sided and truncated sequential detection rules. Several key properties of ODRs are established in this paper, in both the asymptotic regime in which the maximum sample size grows without bound, and the finite regime in which the maximum samples size is a fixed finite number. Furthermore, an extended setup, in which the maximum sample size is a random variable following a geometric distribution whose realization is not revealed to the statistician until observing the last sample, is studied.

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BT - 2014 IEEE International Symposium on Information Theory, ISIT 2014

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Y2 - 29 June 2014 through 4 July 2014

ER -

Opportunistic detection rules

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