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

The estimation of parameter standard errors for semi-variogram models ischallenging, given the two-step process required to fit a parametric model tospatially correlated data. Motivated by an application in thesocial-epidemiology, we focus on exponential semi-variogram models fitted todata between 500 to 2000 observations and little control over the samplingdesign. Previously proposed methods for the estimation of standard errorscannot be applied in this context. Approximate closed form solutions are toocostly using generalized least squares in terms of memory capacities. Thegeneralized bootstrap proposed by Olea and Pardo-Ig\'uzquiza is nonethelessapplicable with weighted instead of generalized least squares. However, thestandard error estimates are hugely biased and imprecise. Therefore, we proposea filtering method added to the generalized bootstrap. The new development ispresented and evaluated with a simulation study which shows that thegeneralized bootstrap with check-based filtering leads to massively improvedresults compared to the quantile-based filter method and previously developedapproaches. We provide a case study using birthweight data.

Parameter uncertainty estimation for exponential semi-variogram models: Two generalized bootstrap methods with check- and quantile-based filtering