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

Voronoi intensity estimators, which are non-parametric estimators forintensity functions of point processes, are both parameter-free and adaptive;the intensity estimate at a given location is given by the reciprocal size ofthe Voronoi/Dirichlet cell containing that location. Their major drawback,however, is that they tend to under-smooth the data in regions where the pointdensity of the observed point pattern is high and over-smooth in regions wherethe point density is low. To remedy this problem, i.e. to find somemiddle-ground between over- and under-smoothing, we propose an additionalsmoothing technique for Voronoi intensity estimators for point processes inarbitrary metric spaces, which is based on repeated independent thinnings ofthe point process/pattern. Through a simulation study we show that ourresample-smoothing technique improves the estimation significantly. Inaddition, we study statistical properties such as unbiasedness and variance,and propose a rule-of-thumb and a data-driven cross-validation approach tochoose the amount of thinning/smoothing to apply. We finally apply our proposedintensity estimation scheme to two datasets: locations of pine saplings (planarpoint pattern) and motor vehicle traffic accidents (linear network pointpattern).

Resample-smoothing of Voronoi intensity estimators