A latent model to detect multiple clusters of varying sizes.

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.

journal_name

Biometrics

journal_title

Biometrics

authors

Xie M,Sun Q,Naus J

doi

10.1111/j.1541-0420.2009.01197.x

subject

Has Abstract

pub_date

2009-12-01 00:00:00

pages

1011-20

issue

4

eissn

0006-341X

issn

1541-0420

pii

BIOM1197

journal_volume

65

pub_type

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