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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
View graph of relations


  • Sung Won Han
  • Wei Jiang
  • Lianjie Shu
  • Kwok-Leung Tsui


Original languageEnglish
Pages (from-to)14-39
Journal / PublicationCommunications in Statistics: Simulation and Computation
Issue number1
Online published10 Oct 2013
Publication statusPublished - 2015


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. Copyright © Taylor & Francis Group, LLC.

Research Area(s)

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

Citation Format(s)