@article{d1d7cf3bfb974062a0ae8b5569338d69,
title = "A nonparametric assessment of properties of space-time covariance functions",
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.",
keywords = "Asymptotic normality, Covariance, Full symmetry, Random field, Separability, Stationarity",
author = "Bo Li and Genton, {Marc G.} and Michael Sherman",
note = "Funding Information: Bo Li is a Postgraduate Scientist, Geophysical Statistical Project, National Center for Atmospheric Research, Boulder, CO 80307 (E-mail:
[email protected]). Marc G. Genton is Professor, Department of Econometrics, University of Geneva, CH-1211 Geneva 4, Switzerland (E-mail:
[email protected]. ch), and Associate Professor, Department of Statistics, Texas A&M University, College Station, TX 77843 (E-mail:
[email protected]). Michael Sherman is Associate Professor, Department of Statistics, Texas A&M University, College Station, TX 77843 (E-mail:
[email protected]). The National Center for Atmospheric Research is sponsored by the National Science Foundation. Genton acknowledges partial support from National Science Foundation grants DMS-0504896 and CMG ATM-0620624. The authors thank the joint editor, the associate editor, and the referees for constructive suggestions that have improved the content and presentation of this article. The authors also thank Dr. Christopher K. Wikle for providing the Pacific Ocean wind data.",
year = "2007",
month = jun,
doi = "10.1198/016214507000000202",
language = "English (US)",
volume = "102",
pages = "736--744",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor & Francis",
number = "478",
}