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) |
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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 | |
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 |
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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