Compositional data are positive multivariate data with unity sum constraint that have
emerged over the last years in numerous scientific fields. Ever since, numerous models and …

We propose and study a class of simple, nonparametric, yet interpretable measures of
conditional dependence, which we call kernel partial correlation (KPC) coefficient, between …

Current lithium-ion battery (LIB) management technique relying solely on the limited time-
domain measurements appears to reach its limit, and incorporating new sensing …

The integration of blockchain technology into supply chain management (SCM) has
emerged as a revolutionary force transforming traditional business operations. This study …

Simplicial-simplicial regression refers to the regression setting where both the responses
and predictor variables lie within the simplex space, ie they are compositional. For this …

We propose to study Compositional Data (CoDa) from the projective geometry viewpoint.
Indeed, CoDa, as equivalence classes of proportional vectors, corresponds to projective …

In this document we discuss the linear constraints imposed to enforce identifiability of the
parameters (Section 1) and the choice of hyperparameters and spline bases (Section 2), we …

We study a partial linear model in which the response is compositional and the predictors
include both compositional and Euclidean variables. We define a partial linear regression …

Researchers need to understand the differences between parametric and nonparametric
regression models and how they work with available information about the relationship …

Statistical analysis on compositional data has gained a lot of attention due to their great
potential of applications. A feature of these data is that they are multivariate vectors that lie in …