Nonparametric composite outlier detection

Weiguang Wang, Yingbin Liang, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Detection of the existence of data streams drawn from outlying distributions among data streams drawn from a typical distribution is investigated. It is assumed that the typical distribution is known and the outlying distribution is unknown. The generalized likelihood ratio test (GLRT) for this problem is constructed. With knowledge of the Kullback-Liebler divergence between the outlier and typical distributions, the GLRT is shown to be exponentially consistent (i.e, the error risk function decays exponentially fast). It is also shown that with knowledge of the Chernoff distance between the outlying and typical distributions, the same risk decay exponent as the parametric model can be achieved by using the GLRT. It is further shown that, without knowledge of the distance between the distributions, there does not exist an exponentially consistent test, although the GLRT with a diminishing threshold can still be consistent.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538639542
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/TerritoryUnited States
CityPacific Grove

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications


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