Functional Principal Component Analysis of Spatiotemporal Point Processes With Applications in Disease Surveillance

Yehua Li, Yongtao Guan

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


In disease surveillance applications, the disease events are modeled by spatiotemporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatiotemporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations to estimate the mean and covariance functions of the latent process. By performing functional principal component analysis to the latent process, we can better understand the correlation structure in the point process. We also propose an empirical Bayes method to predict the latent spatial random effects, which can help highlight hot areas with unusually high event rates. Under an increasing domain and increasing knots asymptotic framework, we establish the asymptotic distribution for the parametric components in the model and the asymptotic convergence rates for the functional principal component estimators. We illustrate the methodology through a simulation study and an application to the Connecticut Tumor Registry data. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1205-1215
Number of pages11
JournalJournal of the American Statistical Association
Issue number507
StatePublished - Sep 2014


  • Composite likelihood
  • Functional data
  • Latent process
  • Semiparametric methods
  • Spatiotemporal data
  • Splines
  • Strong mixing

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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