Han, Sung Won; Jiang, Wei; Shu, Lianjie; Tsui, Kwok-Leung A comparison of likelihood-based spatiotemporal monitoring methods under non-homogenous population size. (English) Zbl 1314.62191 Commun. Stat., Simulation Comput. 44, No. 1, 14-39 (2015). Summary: This article discusses the spatio-temporal 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 spatio-temporal 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. Cited in 2 Documents MSC: 62L99 Sequential statistical methods Keywords:change point detection; generalized likelihood ratio; non-homogenous Poisson; scan statistics; spatio-temporal surveillance; weighted likelihood ratio PDFBibTeX XMLCite \textit{S. W. Han} et al., Commun. Stat., Simulation Comput. 44, No. 1, 14--39 (2015; Zbl 1314.62191) Full Text: DOI References: [1] DOI: 10.1007/978-3-7908-2846-7_11 · doi:10.1007/978-3-7908-2846-7_11 [2] DOI: 10.1002/qre.1056 · doi:10.1002/qre.1056 [3] DOI: 10.1007/978-1-4612-1686-5 · doi:10.1007/978-1-4612-1686-5 [4] DOI: 10.1111/1467-985X.00186 · Zbl 1002.62517 · doi:10.1111/1467-985X.00186 [5] DOI: 10.1002/qre.1287 · doi:10.1002/qre.1287 [6] DOI: 10.1093/biomet/asq010 · Zbl 1406.62088 · doi:10.1093/biomet/asq010 [7] DOI: 10.5705/ss.2011.027a · Zbl 1214.62017 · doi:10.5705/ss.2011.027a [8] Montgomery D.C., Statistical Quality Control. 5th ed (2005) · Zbl 1059.62125 [9] DOI: 10.1002/sim.4780080306 · doi:10.1002/sim.4780080306 [10] Royall R.M., Statistical Evidence: A Likelihood Paradigm (1997) [11] Ryan A.G., Journal of Quality Technology 42 pp 260– (2010) [12] DOI: 10.1002/sim.4122 · doi:10.1002/sim.4122 [13] DOI: 10.1080/07408170902942667 · doi:10.1080/07408170902942667 [14] DOI: 10.1002/sim.2898 · doi:10.1002/sim.2898 [15] DOI: 10.1111/1467-985X.00256 · doi:10.1111/1467-985X.00256 [16] Tartakovsky A.G., Applications of Sequential Methodologies pp pp. 331– (2004) [17] DOI: 10.1109/TAES.1981.309178 · doi:10.1109/TAES.1981.309178 [18] DOI: 10.1080/0740817X.2011.582476 · doi:10.1080/0740817X.2011.582476 [19] Tsui K.-L., IIE Transactions 60 pp 49– (2011) [20] DOI: 10.1111/j.1467-985X.2011.00714.x · Zbl 06009937 · doi:10.1111/j.1467-985X.2011.00714.x [21] Woodall W.H., Technometrics 38 pp 291– (1985) [22] DOI: 10.1080/00401706.1989.10488555 · doi:10.1080/00401706.1989.10488555 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.