Deviation detection with continuous observations

Pengfei Yang, Biao Chen

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages537-541
Number of pages5
ISBN (Print)9781479975914
DOIs
StatePublished - Feb 23 2016
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: Dec 13 2015Dec 16 2015

Other

OtherIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
CountryUnited States
CityOrlando
Period12/13/1512/16/15

Keywords

  • Deviation detection
  • KL divergence
  • Lévy metric
  • δ-optimality

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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  • Cite this

    Yang, P., & Chen, B. (2016). Deviation detection with continuous observations. In 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 (pp. 537-541). [7418253] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2015.7418253