@inproceedings{31f72e6ee5ad46d0a5c3bd9f6a94dfb6,
title = "Deviation detection with continuous observations",
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",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 ; Conference date: 13-12-2015 Through 16-12-2015",
year = "2016",
month = feb,
day = "23",
doi = "10.1109/GlobalSIP.2015.7418253",
language = "English (US)",
series = "2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "537--541",
booktitle = "2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015",
}