@inproceedings{09ecaa1f7fb84dc9a6fc732374b99da8,
title = "Asymptotically achievable error probabilities for multiple hypothesis testing",
abstract = "The region of achievable error probabilities for k-ary hypothesis tests is studied in the asymptotic setting with n independent and identically distributed observations. We identify a k2 - k - 1 dimensional parametric family of optimal (non-dominated) tests and relate it to a family of Bayes tests whose loss function depends exponentially on n. We asymptotically characterize the conditional error probabilities for these tests within O(1) as n → ∞, using strong large deviations analysis.",
author = "Pierre Moulin",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Symposium on Information Theory, ISIT 2016 ; Conference date: 10-07-2016 Through 15-07-2016",
year = "2016",
month = aug,
day = "10",
doi = "10.1109/ISIT.2016.7541557",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1541--1545",
booktitle = "Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory",
address = "United States",
}