KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Permanent link to this recordhttp://hdl.handle.net/10754/631988
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AbstractThe joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non-Gaussian. In this paper, we introduce a copula-based spatio-temporal model for analyzing spatio-temporal data and propose a semiparametric estimator. The proposed algorithm is computationally simple, since it models the marginal distribution and the spatio-temporal dependence separately. Instead of assuming a parametric distribution, the proposed method models the marginal distributions nonparametrically and thus offers more flexibility. The method also provides a convenient way to construct both point and interval predictions at new times and new locations, based on the estimated conditional quantiles. Through a simulation study and an analysis of wind speeds observed along the border between Oregon and Washington, we show that our method produces more accurate point and interval predictions for skewed data than those based on normality assumptions. This article is protected by copyright. All rights reserved.
CitationTang Y, Wang HJ, Sun Y, Hering AS (2019) Copula-based semiparametric models for spatio-temporal data. Biometrics. Available: http://dx.doi.org/10.1111/biom.13066.
SponsorsThe authors would like to thank two reviewers, an associate editor, and the editor for constructive comments and helpful suggestions. The work is partially supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research award OSR-2015-CRG4-2582, the National Science Foundation grant DMS-1712760, the IR/D program from the National Science Foundation, National Natural Science Foundation of China grants 11871376 and 11801355, Shanghai Pujiang Program 18PJ1409800, and Key Laboratory for Applied Statistics of MOE, Northeast Normal University 130028849. Any opinion, findings, and conclusions or recommendations ex-pressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.