Spatial and Space-Time Processes

Jan Beran, Yuanhua Feng, Sucharita Ghosh and Rafal Kulik
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Jan Beran: University of Konstanz, Dept. of Mathematics and Statistics
Yuanhua Feng: University of Paderborn, Faculty of Business Administration and Economics
Sucharita Ghosh: Swiss Federal Research Institute WSL
Rafal Kulik: University of Ottawa, Dept. of Mathematics and Statistics

Chapter Chapter 9 in Long-Memory Processes, 2013, pp 753-769 from Springer

Abstract: Abstract Spatial data play an important role in many areas such as ecology, biology, environmental monitoring, agronomy, remote sensing, geology, to name a few. Sometimes observations are obtained on a regular lattice (see, e.g. Whittle in Biometrika 49:305–314, 1962; Bartlett in Adv. Appl. Prob. 6(2):336–358, 1974; Besag in J. R. Stat. Soc., Ser. B 36(2):192–236, 1974; Cressie in Statistics for spatial data, 1993; Christakos in Random field models in Earth sciences, 1992; Modern spatiotemporal geostatistics, 2000; Benson et al. in Water Resour. Res. 42(W01415):1–18, 2006; Sain and Cressie in J. Econom. 140(1):226–259, 2007, and references therein). This leads to considering spatial processes on a grid, or more specifically, random fields X t with index $t\in\mathbb{Z}^{2}$ . On the other hand, if the spatial points are not on a regular grid, then random fields with $t\in\mathbb{R}^{2}$ are used.

Keywords: Random Field; Species Occurrence; Background Process; Autocovariance Function; Cumulative Probability Distribution Function (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-35512-7_9

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