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A Latent Model to Detect Multiple Clusters of Varying Sizes
Author(s) -
Xie Minge,
Sun Qiankun,
Naus Joseph
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01197.x
Subject(s) - computer science , inference , cluster analysis , model selection , expectation–maximization algorithm , data mining , scan statistic , monte carlo method , statistic , latent variable model , latent variable , artificial intelligence , algorithm , maximum likelihood , statistics , mathematics
Summary 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.