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

We introduce computational methods that allow for effective estimation of aflexible, parametric non-stationary spatial model when the field size is toolarge to compute the multivariate normal likelihood directly. In this method,the field is defined as a weighted spatially varying linear combination of aglobally stationary process and locally stationary processes. Often in such amodel, the difficulty in its practical use is in the definition of theboundaries for the local processes, and therefore we describe one suchselection procedure that generally captures complex non-stationaryrelationships. We generalize the use of stochastic approximation to the scoreequations for data on a partial grid in this non-stationary case and providetools for evaluating the approximate score in $O(n\log n)$ operations and$O(n)$ storage. We perform various simulations to explore the effectiveness andspeed of the proposed methods and conclude by making inference on theaccumulation behavior of arsenic applied to a sand grain.

Non-Stationary Covariance Estimation using the Stochastic Score Approximation for Large Spatial Data