title: Modeling nonstationarity in space and time.
abstract: We propose to model a spatio-temporal random field that has nonstationary covariance structure in both space and time domains by applying the concept of the dimension expansion method in Bornn et al. (2012). Simulations are conducted for both separable and nonseparable space-time covariance models, and the model is also illustrated with a streamflow dataset. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance models or models that are nonstationary in space but stationary in time.
authors.affiliation: Department of Statistics, University of Illinois at Urbana-Champaign, Illinois, U.S.A.
authors.forename: Lyndsay
authors.initials: L
authors.lastname: Shand
authors.affiliation: Department of Statistics, University of Illinois at Urbana-Champaign, Illinois, U.S.A.
authors.forename: Bo
authors.initials: B
authors.lastname: Li
mesh_terms: D015233:Models, Statistical; D013997:Time Factors
keywords: Dimension expansion; Nonstationarity; Space-time random field