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

Probability density functions (PDFs) can be understood as continuouscompositions by the theory of Bayes spaces. The origin of a Bayes space isdetermined by a given reference measure. This can be easily changed through thewell-known chain rule which has an impact on the geometry of the Bayes space.This work provides a mathematical framework for setting a reference measure. Itis used to develop a weighting scheme on the bounded domain of distributionaldata. The impact on statistical analysis is shown from the perspective ofsimplicial functional principal component analysis. Moreover, a novel centeredlog-ratio transformation is proposed to map a weighted Bayes spaces into anunweighted $L^2$ space, enabling to use most tools developed in functional dataanalysis (e.g. clustering, regression analysis, etc.) while accounting for theweighting strategy. The potential of our proposal is shown through simulationand on a real case study using Italian income data.

Changing reference measure in Bayes spaces with applications to functional data analysis