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

Compositional data are common in many fields, both as outcomes and predictorvariables. The inventory of models for the case when both the outcome andpredictor variables are compositional is limited and the existing models aredifficult to interpret, due to their use of complex log-ratio transformations.We develop a transformation-free linear regression model where the expectedvalue of the compositional outcome is expressed as a single Markov transitionfrom the compositional predictor. Our approach is based on generalized methodof moments thereby not requiring complete specification of data likelihood andis robust to different data generating mechanism. Our model is simple tointerpret, allows for 0s and 1s in both the compositional outcome andcovariates, and subsumes several interesting subcases of interest. We alsodevelop a permutation test for linear independence. Finally, we show thatdespite its simplicity, our model accurately captures the relationship betweencompositional data from education and medical research.

A Transformation-free Linear Regression for Compositional Outcomes and Predictors