The interpretation of regression models with compositional vectors as response and/or
explanatory variables has been approached from different perspectives. The initial …

Compositional data are common in many fields, both as outcomes and predictor variables.
The inventory of models for the case when both the outcome and predictor variables are …

In the framework of Compositional Data Analysis, vectors carrying relative information, also
called compositional vectors, can appear in regression models either as dependent or as …

In an election, the vote shares by party for a given subdivision of a territory form a
compositional vector (positive components adding up to 1). Conventional multiple linear …

We consider models for network-indexed multivariate data, also known as graph signals,
involving a dependence between variables as well as across graph nodes. The …

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 …

Compositional data find broad application across diverse fields due to their efficacy in
representing proportions or percentages of various components within a whole. Spatial …

The ongoing crisis of climate change necessitates the development of effective methods for
monitoring and mapping environmental features and species to ensure their preservation …

The objective of this thesis is to investigate the outcome of an election and the impacts of the
socio-economics factors on the vote shares in the multiparty system from mathematical point …

To model multivariate, possibly heavy-tailed data, we compare the multivariate normal
model (N) with two versions of the multivariate Student model: the independent multivariate …