Opportunistic detection under a fixed-sample-size setting

Wenyi Zhang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


With a finite number of samples drawn from one of two possible distributions sequentially revealed, an opportunistic detection rule is proposed, which possibly makes an early decision in favor of the alternative hypothesis, while always deferring the decision of the null hypothesis until collecting all the samples. Properties of this opportunistic detection rule are discussed and its key asymptotic behavior in the large sample size limit is established. Specifically, a Chernoff-Stein lemma type of characterization of the exponential decay rate of the miss probability under the Neyman-Pearson criterion is established, and consequently, a performance metric of asymptotic exponential efficiency loss is proposed and discussed, which is exactly the ratio between the Kullback-Leibler distance and the Chernoff information of the two hypotheses. Analytical results are corroborated by numerical experiments.

Original languageEnglish (US)
Article number6338300
Pages (from-to)1107-1114
Number of pages8
JournalIEEE Transactions on Information Theory
Issue number2
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Chernoff information
  • Chernoff-Stein lemma
  • opportunistic detection
  • sequential detection
  • stopping time


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