A latent model to detect multiple clusters of varying sizes

Minge Xie, Qiankun Sun, Joseph Naus

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1011-1020
Number of pages10
JournalBiometrics
Volume65
Issue number4
DOIs
StatePublished - Dec 1 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Keywords

  • AIC and BIC criteria
  • Clustering events
  • EM algorithm
  • Latent model
  • Likelihood inference
  • MCMC algorithm
  • Scan statistics
  • Temporal samples

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