TY - GEN
T1 - Universal multiple outlier hypothesis testing
AU - Li, Yun
AU - Nitinawarat, Sirin
AU - Veeravalli, Venugopal V.
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - The universal multiple outlier hypothesis testing problem is studied in two settings. In the first setting, each outlier can be arbitrarily distributed, and the number of outliers is fixed and known. In the second setting, the number of outliers is unknown at the outset. Nothing is known about the typical and outlier distributions other than that they are different and have full supports. For the first setting, a universally exponentially consistent test is proposed, and its achievable error exponent is characterized. The limiting error exponent achieved by such test is analyzed as the number of coordinates goes to infinity, and it is shown that the test also enjoys universally asymptotically exponential consistency. For the second setting, it is shown that with the assumption of outliers being identically distributed and the exclusion of the null hypothesis, a test based on the generalize likelihood principle is universally exponentially consistent.
AB - The universal multiple outlier hypothesis testing problem is studied in two settings. In the first setting, each outlier can be arbitrarily distributed, and the number of outliers is fixed and known. In the second setting, the number of outliers is unknown at the outset. Nothing is known about the typical and outlier distributions other than that they are different and have full supports. For the first setting, a universally exponentially consistent test is proposed, and its achievable error exponent is characterized. The limiting error exponent achieved by such test is analyzed as the number of coordinates goes to infinity, and it is shown that the test also enjoys universally asymptotically exponential consistency. For the second setting, it is shown that with the assumption of outliers being identically distributed and the exclusion of the null hypothesis, a test based on the generalize likelihood principle is universally exponentially consistent.
UR - http://www.scopus.com/inward/record.url?scp=84894198101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894198101&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2013.6714036
DO - 10.1109/CAMSAP.2013.6714036
M3 - Conference contribution
AN - SCOPUS:84894198101
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 177
EP - 180
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -