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Tailen Hsing, University of Michigan

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Analyzing Spatial Data Locally
07 April 2016 from 4:00 PM to 5:00 PM
201 Thomas Building
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Tailen Hsing 

Stationarity is a common assumption in spatial statistics. The justification is often that stationarity is a reasonable approximation if we focus on spatial data "locally." In this talk we first review various known approaches for modeling nonstationary spatial data. We then examine the notion of local stationarity in more detail. In particular, we will consider a nonstationary model whose covariance behaves like the Matérn covariance locally and an inference approach for that model based on gridded data

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