Half-spectral analysis of spatial-temporal data: The case study of Iranian daily wind speed data. Issue 3 (9th March 2020)

• Record Type: Journal Article
• Authors: Shahnavaz, Ali; Mosammam, Ali M.; Behzadi, Mohammad Hassan
• Is Part Of: Communications in statistics. Volume 51:Issue 3(2022)
• Page Start: 1103
• Page End: 1120
• Publication Date: 2020-03-09
• Subjects: Half-spectral model -- Space-time model -- Whittle likelihood; 62M30; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2019.1664572
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: In this paper, we first study the theory of the spatial-temporal half spectral modeling and describe some properties of recently proposed half spectral models. Next, we propose an estimation method for the estimation of spatial-temporal covariance functions in the half-spectral setting. To assess the performance of the proposed half-spectral models, we conduct two simulations, in which we compare the proposed fitting approach with respect to the other classical estimation methods. The proposed methods have great success in fitting parametric space-time covariance functions specifically for massive data sets. Finally, we apply the proposed methods for a real daily wind speed data in Iran.