A nonparametric assessment of properties of space-time covariance functions

Bo Li, Marc G. Genton, Michael Sherman

Research output: Contribution to journalArticle

Abstract

We propose a unified framework for testing various assumptions commonly made for covariance functions of stationary spatio-temporal random fields. The methodology is based on the asymptotic normality of space-time covariance estimators. We focus on tests for full symmetry and separability in this article, but our framework naturally covers testing for isotropy and Taylor"s hypothesis. Our test successfully detects the asymmetric and nonseparable features in two sets of wind speed data. We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on space-time covariance functions.

Original languageEnglish (US)
Pages (from-to)736-744
Number of pages9
JournalJournal of the American Statistical Association
Volume102
Issue number478
DOIs
StatePublished - Jun 1 2007

Keywords

  • Asymptotic normality
  • Covariance
  • Full symmetry
  • Random field
  • Separability
  • Stationarity

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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