Abstract
A continuous spatial model can be constructed by convolving a very simple, perhaps independent, process with a kernel or point spread function. This approach for constructing a spatial process offers a number of advantages over specification through a spatial covariogram. In particular, this process convolution specification leads to computational simplifications and easily extends beyond simple stationary models. This paper uses process convolution models to build space and space-time models that are flexible and able to accommodate large amounts of data. Data from environmental monitoring is considered.
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Higdon, D. (2002). Space and Space-Time Modeling using Process Convolutions. In: Anderson, C.W., Barnett, V., Chatwin, P.C., El-Shaarawi, A.H. (eds) Quantitative Methods for Current Environmental Issues. Springer, London. https://doi.org/10.1007/978-1-4471-0657-9_2
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DOI: https://doi.org/10.1007/978-1-4471-0657-9_2
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