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Activity Number:
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488
- Nonstationary and Anisotropic Spatial Processes
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #330671
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Presentation
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Title:
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Bayesian Inference for Geometrically Anisotropic Spatial Random Fields on Regular Lattice
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Author(s):
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Fan Dai* and Somak Dutta
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Companies:
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and Iowa State University
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Keywords:
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geostatistics; spectral density; matrix-free computation; discrete cosine transformation; kriging; satellite measurements
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Abstract:
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Geometric anisotropy arises when the variogram of a spatial random field varies with direction. We propose a Bayesian inference for geometrically anisotropic random fields on regular lattice. The class of random fields we focus on arises from fractional Laplacian differencing on the lattice. Furthermore, with diminishing lattice spacing, these fields approximate certain continuum anisotropic Matern class of models. We demonstrate our methodology by analyzing data on ocean chlorophyll concentrations obtained from MODIS-Aqua project of NASA.
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Authors who are presenting talks have a * after their name.
