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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Generally spatialregressionconsiders onlyone of the spatial effects, namely spatial dependence or heteroskedasticity between areas. Spatial autoregressive(SAR) models take only into account thedependence on the response variable. Most of SAR estimators are valid if there is no violation in the error assumption. Estimation of SAR parameters with heteroskedasticity using maximum likelihood (ML)method gives bias and inconsistent estimators. An alternative method that can be used is Bayesian method. Bayesian method solves heteroskedasticity by modeling the structure of variance-covariance matrix. Simulation data is used to evaluate the Bayesian method in estimating parameters of SAR model with heteroskedasticity. The results indicate that Bayesian method provides bias parameter estimates relatively small and consistent compared to the ML method.

Simulation Studyfor Comparison of Maximum Likelihood and Bayesian Method in Spatial Autoregressive Models with Heteroskedasticity