Abstract:
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The spatial statistical analysis of remote-sensing datasets poses several challenges. The datasets are large or even massive, which leads to computational infeasibility. Often, it is advantageous to combine ("fuse") measurements on the same or related spatial processes from several instruments, but these instruments typically exhibit different spatial footprints and measurement-error characteristics. In addition, complementary, massive datasets might be stored in different locations and are costly to move to one location, which means that the analysis must be moved to the data, instead of the other way around. I will discuss how all of these problems can be tackled using statistical models that can be written as linear combinations of spatial basis functions at multiple resolutions. These basis functions can represent arbitrary processes, allow change-of-support, and enable scalable, parallel, and distributed computations.
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