A comparison of likelihood-based spatiotemporal monitoring methods under non-homogenous population size

Sung Won Han, Wei Jiang, Lianjie Shu, Kwok Leung Tsui

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This article discusses the spatiotemporal surveillance problem of detecting rate changes of Poisson data considering non-homogenous population sample size. By applying Monte Carlo simulations, we investigate the performance of several likelihood-based approaches under various scenarios depending on four factors: (1) population trend, (2) change magnitude, (3) change coverage, and (4) change time. Our article evaluates the performance of spatiotemporal surveillance methods based on the average run length at different change times. The simulation results show that no method is uniformly better than others in all scenarios. The difference between the generalized likelihood ratio (GLR) approach and the weighted likelihood ratio (WLR) approach depends mainly on population size, not change coverage, change magnitude, or change time. We find that changes associated with a small population in time periods and/or spatial regions favor the WLR approach, but those associated with a large population favor the GLR under any trends of population changes.

Original languageEnglish
Pages (from-to)14-39
Number of pages26
JournalCommunications in Statistics: Simulation and Computation
Issue number1
Publication statusPublished - 2015 Jan 2
Externally publishedYes


  • Change point detection
  • Generalized likelihood ratio
  • Non-homogenous Poisson
  • Scan statistics
  • Spatiotemporal surveillance
  • Weighted likelihood ratio

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation


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