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Activity Number: 488 - Nonstationary and Anisotropic Spatial Processes
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #330671 Presentation
Title: Bayesian Inference for Geometrically Anisotropic Spatial Random Fields on Regular Lattice
Author(s): Fan Dai* and Somak Dutta
Companies: and Iowa State University
Keywords: geostatistics; spectral density; matrix-free computation; discrete cosine transformation; kriging; satellite measurements

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.

Authors who are presenting talks have a * after their name.

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