Non-stationary variance models for nitrous oxide emissions from soil, by spectral tempering
by Kathy Haskard
Abstract: In geostatistical spatial modelling, data are typically assumed to have stationary covariance -- because with only a single realisation of a random process it is difficult to estimate a covariance model that is different at different locations. However this assumption can be untenable in varied landscapes, and estimates of the variance of predictions can be badly affected. This is important, for example, in assessing climate change scenarios. Spectral tempering allows flexible spatial adaptation of spatial covariance; smoothness and variances can be adapted independently, with parameters that are simple and interpretable. This talk begins with an introduction to some geostatistical concepts, then describes spectral tempering in a graphical way, illustrating and validating with application to data on soil emissions of nitrous oxide, an important greenhouse gas. The method can be applied to many kinds of spatially-correlated data.
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