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

In this paper, we propose a Spatial Robust Mixture Regression model toinvestigate the relationship between a response variable and a set ofexplanatory variables over the spatial domain, assuming that the relationshipsmay exhibit complex spatially dynamic patterns that cannot be captured byconstant regression coefficients. Our method integrates the robust finitemixture Gaussian regression model with spatial constraints, to simultaneouslyhandle the spatial nonstationarity, local homogeneity, and outliercontaminations. Compared with existing spatial regression models, our proposedmodel assumes the existence a few distinct regression models that are estimatedbased on observations that exhibit similar response-predictor relationships. Assuch, the proposed model not only accounts for nonstationarity in the spatialtrend, but also clusters observations into a few distinct and homogenousgroups. This provides an advantage on interpretation with a few stationarysub-processes identified that capture the predominant relationships betweenresponse and predictor variables. Moreover, the proposed method incorporatesrobust procedures to handle contaminations from both regression outliers andspatial outliers. By doing so, we robustly segment the spatial domain intodistinct local regions with similar regression coefficients, and sporadiclocations that are purely outliers. Rigorous statistical hypothesis testingprocedure has been designed to test the significance of such segmentation.Experimental results on many synthetic and real-world datasets demonstrate therobustness, accuracy, and effectiveness of our proposed method, compared withother robust finite mixture regression, spatial regression and spatialsegmentation methods.

Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence