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

Spatial small area estimation models have become very popular in somecontexts, such as disease mapping. Data in disease mapping studies areexhaustive, that is, the available data are supposed to be a complete registerof all the observable events. In contrast, some other small area studies do notuse exhaustive data, such as survey based studies, where a particular samplingdesign is typically followed and inferences are later extrapolated to theentire population. In this paper we propose a spatial model for small areasurvey studies, taking advantage of spatial dependence between units, which isthe key assumption used for yielding reliable estimates in exhaustive databased studies. In addition, and in contrast to most spatial survey studies, wetake the approach of also considering information on the sampling design andadditional supplementary variables in order to yield small area estimates. Thismakes it possible to merge spatial and sampling based approaches into a commonproposal

Taking advantage of sampling deisgns in Bayesian spatial small ares survey studies