Title :
Opportunistic detection rules
Author :
Wenyi Zhang ; Moustakides, George V. ; Poor, H. Vincent
Author_Institution :
Dept. of EEIS, Univ. of Sci. & Technol. of China, Hefei, China
fDate :
June 29 2014-July 4 2014
Abstract :
Opportunistic detection rules (ODRs) are variants of fixed-sample-size detection rules in which the statistician is allowed to make an early decision on the alternative hypothesis opportunistically based on the sequentially observed samples. From a sequential decision perspective, ODRs are also mixtures of one-sided and truncated sequential detection rules. Several key properties of ODRs are established in this paper, in both the asymptotic regime in which the maximum sample size grows without bound, and the finite regime in which the maximum samples size is a fixed finite number. Furthermore, an extended setup, in which the maximum sample size is a random variable following a geometric distribution whose realization is not revealed to the statistician until observing the last sample, is studied.
Keywords :
sampling methods; sequential estimation; fixed sample size detection rule variant; geometric distribution; maximum sample size; one sided sequential detection rule; opportunistic detection rules; random variable; sequential decision; sequentially observed sample; truncated sequential detection rule; Bayes methods; Educational institutions; Electronic mail; Error probability; Frequency selective surfaces; Information theory; Random variables;
Conference_Titel :
Information Theory (ISIT), 2014 IEEE International Symposium on
Conference_Location :
Honolulu, HI
DOI :
10.1109/ISIT.2014.6874932
