Time-varying correlation structure estimation and local-feature detection for spatio-temporal data

Xueying Zheng, Lan Xue, Annie Qu

Research output: Contribution to journalArticlepeer-review

Abstract

Spatial–temporal data arise frequently in biomedical, environmental, political and social science studies. Capturing dynamic changes of time-varying correlation structure is scientifically important in spatio-temporal data analysis. We approximate the time-varying empirical estimator of the spatial correlation matrix by groups of selected basis matrices representing substructures of the correlation matrix. After projecting the correlation structure matrix onto a space spanned by basis matrices, we also incorporate varying-coefficient model selection and estimation for signals associated with relevant basis matrices. The unique feature of the proposed method is that signals at local regions corresponding with time can be identified through the proposed penalized objective function. Theoretically, we show model selection consistency and the oracle property in detecting local signals for the varying-coefficient estimators. The proposed method is illustrated through simulation studies and brain fMRI data.

Original languageEnglish (US)
Pages (from-to)221-239
Number of pages19
JournalJournal of Multivariate Analysis
Volume168
DOIs
StatePublished - Nov 2018

Keywords

  • Local feature
  • Longitudinal data
  • Penalty
  • Varying-coefficient model
  • fMRI

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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