Modeling and Analysis of Compositional Data presents a practical and comprehensive
introduction to the analysis of compositional data along with numerous examples to illustrate …

We review recent advances in Object Oriented Spatial Statistics, a system of ideas,
algorithms and methods that allows the analysis of high dimensional and complex data …

The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …

The Wasserstein metric, Wasserstein–Fréchet mean, simulation results and additional
proofs. The supplementary material includes additional discussion on the Wasserstein …

Recent developments in the probabilistic and statistical analysis of probability density
functions are reviewed. Density functions are treated as data objects for which suitable …

The log-ratio approach to compositional data (CoDa) analysis has now entered a mature
phase. The principles and statistical tools introduced by J. Aitchison in the eighties have …

Probability density functions are frequently used to characterize the distributional properties
of large-scale database systems. As functional compositions, densities primarily carry …

A Bayes linear space is a linear space of equivalence classes of proportional σ‐finite
measures, including probability measures. Measures are identified with their density …

Data consisting of time‐indexed distributions of cross‐sectional or intraday returns have
been extensively studied in finance, and provide one example in which the data atoms …

The advance of sensor and information technologies is leading to data-rich industrial
environments, where large amounts of data are potentially available. This study focuses on …