A latent model to detect multiple clusters of varying sizes.
This article develops a latent model and likelihood-based inference to detect temporal clustering of events. The model mimics typical processes generating the observed data. We apply model selection techniques to determine the number of clusters, and develop likelihood inference and a Monte Carlo expectation-maximization algorithm to estimate model parameters, detect clusters, and identify cluster locations. Our method differs from the classical scan statistic in that we can simultaneously detect multiple clusters of varying sizes. We illustrate the methodology with two real data applications and evaluate its efficiency through simulation studies. For the typical data-generating process, our methodology is more efficient than a competing procedure that relies on least squares.
Xie
Minge
M
Department of Statistics, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA. mxie@stat.rutgers.edu
Sun
Qiankun
Q
Naus
Joseph
J
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D002006:Brucellosis / Q000453:epidemiology; D016000:Cluster Analysis; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D014481:United States / Q000453:epidemiology