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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

One of the nonparametric methods to estimate the probability density is kernel method. In this paper, kernel density estimation methods including the naive kernel(NK) estimator and geometric extrapolation based kernel(GEBK) method are introduced and discussed. Theoretical properties, including the selection of smoothing parameter, the accuracy of resultant estimators using Monte Carlo simulation are studied. The results show that the amount of bias in the proposed geometric extrapolation based kernel estimator significantly decreases.

The bias reduction in density estimation using a geometric extrapolated kernel estimator