English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A stationary spatial model is an idealization and we expect that the truedependence structures of physical phenomena are spatially varying, but howshould we handle this non-stationarity in practice? We study the challengesinvolved in applying a flexible non-stationary model to a dataset of annualprecipitation in the conterminous US, where exploratory data analysis showsstrong evidence of a non-stationary covariance structure.  The aim of this paper is to investigate the modelling pipeline oncenon-stationarity has been detected in spatial data. We show that there is areal danger of over-fitting the model and that careful modelling is necessaryin order to properly account for varying second-order structure. In fact, theexample shows that sometimes non-stationary Gaussian random fields are notnecessary to model non-stationary spatial data.

Does non-stationary spatial data always require non-stationary random fields?