Abstract

Stationarity is a common assumption in spatial statistics. The justification is often that stationarity is a reasonable approximation if data are collected "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 spatial model whose covariance behaves like the Matern covariance locally and an inference approach for that model based on dense gridded data.