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Abstract:
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Computational efficiency is at the forefront of many cutting edge spatial modeling techniques. Non-stationarity, on the other hand, has often been an unintentional feature of the approximations used in these spatial models. Deriving from the well known multivariate linear regression model, we propose a non-stationary and non-isotropic spatial model. In order to remain relevant with today's massive datasets challenges, we apply the concept of nearest-neighbors to our normal-inverse-Wishart framework. The model, called Nearest-Neighbor Gaussian Process with Random Covariance matrices (NN-RCM) is developed for both univariate and multivariate spatial settings and allow for specific characteristics such as duplicate observations and missing data. The model is illustrated in a case study of albedo assessments over CONUS from the Geostationary Operational Environmental Satellites (GOES) East and West. We apply the bivariate NN-RCM model using each satellite as a source of information. The objective is to merge the albedo assessments while also quantifying the discrepancy between the sources.
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