Deviation detection with continuous observations

Pengfei Yang; Biao Chen

doi:10.1109/GlobalSIP.2015.7418253

Deviation detection with continuous observations

Pengfei Yang, Biao Chen

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

1 Scopus citations

Abstract

This paper considers the detection of possible deviation from a nominal distribution for continuously valued random variables. Specifically, under the null hypothesis, samples are distributed approximately according to a nominal distribution. Any significant departure from this nominal distribution constitutes the alternative hypothesis. It is established that for such deviation detection where the nominal distribution is only specified under the null hypothesis, Kullback-Leibler distance is not a suitable measure for deviation. Subsequently, Lévy metric is adopted and an asymptotically δ-optimal detector is identified for this problem.

Original language	English (US)
Title of host publication	2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	537-541
Number of pages	5
ISBN (Print)	9781479975914
DOIs	https://doi.org/10.1109/GlobalSIP.2015.7418253
State	Published - Feb 23 2016
Event	IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States Duration: Dec 13 2015 → Dec 16 2015

Other

Other	IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
Country/Territory	United States
City	Orlando
Period	12/13/15 → 12/16/15

Keywords

Deviation detection
KL divergence
Lévy metric
δ-optimality

ASJC Scopus subject areas

Information Systems
Signal Processing

Access to Document

10.1109/GlobalSIP.2015.7418253

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Yang, P & Chen, B 2016, Deviation detection with continuous observations. in 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015., 7418253, Institute of Electrical and Electronics Engineers Inc., pp. 537-541, IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015, Orlando, United States, 12/13/15. https://doi.org/10.1109/GlobalSIP.2015.7418253

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abstract = "This paper considers the detection of possible deviation from a nominal distribution for continuously valued random variables. Specifically, under the null hypothesis, samples are distributed approximately according to a nominal distribution. Any significant departure from this nominal distribution constitutes the alternative hypothesis. It is established that for such deviation detection where the nominal distribution is only specified under the null hypothesis, Kullback-Leibler distance is not a suitable measure for deviation. Subsequently, L{\'e}vy metric is adopted and an asymptotically δ-optimal detector is identified for this problem.",

keywords = "Deviation detection, KL divergence, L{\'e}vy metric, δ-optimality",

author = "Pengfei Yang and Biao Chen",

year = "2016",

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doi = "10.1109/GlobalSIP.2015.7418253",

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booktitle = "2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015",

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N2 - This paper considers the detection of possible deviation from a nominal distribution for continuously valued random variables. Specifically, under the null hypothesis, samples are distributed approximately according to a nominal distribution. Any significant departure from this nominal distribution constitutes the alternative hypothesis. It is established that for such deviation detection where the nominal distribution is only specified under the null hypothesis, Kullback-Leibler distance is not a suitable measure for deviation. Subsequently, Lévy metric is adopted and an asymptotically δ-optimal detector is identified for this problem.

AB - This paper considers the detection of possible deviation from a nominal distribution for continuously valued random variables. Specifically, under the null hypothesis, samples are distributed approximately according to a nominal distribution. Any significant departure from this nominal distribution constitutes the alternative hypothesis. It is established that for such deviation detection where the nominal distribution is only specified under the null hypothesis, Kullback-Leibler distance is not a suitable measure for deviation. Subsequently, Lévy metric is adopted and an asymptotically δ-optimal detector is identified for this problem.

KW - Deviation detection

KW - KL divergence

KW - Lévy metric

KW - δ-optimality

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U2 - 10.1109/GlobalSIP.2015.7418253

DO - 10.1109/GlobalSIP.2015.7418253

M3 - Conference contribution

AN - SCOPUS:84964724201

SN - 9781479975914

SP - 537

EP - 541

BT - 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Y2 - 13 December 2015 through 16 December 2015

ER -

Deviation detection with continuous observations

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