A False Discovery Rate Oriented Approach to Parallel Sequential Change Detection Problems

Jie Chen, Wenyi Zhang, H. V. Vincent Poor

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The problem of sequentially detecting changes in parallel data streams is formulated and investigated. Each data stream may have its own change point at which the underlying probability distribution of its data changes, and the decision maker needs to declare, sequentially, which data streams have passed their change points. With a large number of parallel data streams, the error metric is the false discovery rate (FDR), which is the expected ratio of the number of falsely declared data streams to the total number of declared data streams. A data stream is falsely declared if the detected change point is ahead of its actual change point. Decision procedures that are guaranteed to control the FDR level are developed, and it is also shown that the average decision delays (ADDs) of these decision procedures do not grow with the number of data streams. Numerical simulations and case studies are conducted to corroborate the analytical results, and to illustrate the utility of the decision procedures.

Original languageEnglish (US)
Article number9020141
Pages (from-to)1823-1836
Number of pages14
JournalIEEE Transactions on Signal Processing
StatePublished - 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Average decision delay
  • false discovery rate
  • large-scale inference
  • multiple change detection
  • multiple hypothesis testing


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