14 citations · 35 across the 13 of their papers we have counts for
14 papers
Random mixtures in Bayes Hilbert spaces
Giulia Patanè, Sonja Greven, Alessandra Menafoglio
We present a framework for the analysis and unmixing of random density mixtures in the Bayes Hilbert space. General identifiability results for mixtures in Hilbert spaces are estab…
K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles
Giulia Patanè, Alessandra Menafoglio, Alexander Krauth +4
Existing clustering methods for functional data often prioritize partitioning accuracy over interpretability, making it challenging to extract meaningful insights when the data-gen…
A Convolution Process for Sea Surface Temperature Hot-Spot Identification in the Mediterranean Sea
Leonardo Marchesin, Alessandra Menafoglio, Piercesare Secchi
Sea surface temperature (SST) is a fundamental determinant of global climate dynamics and economic activity. Reliable projections of future SST patterns depend critically on a rigo…
Functional-Ordinal Canonical Correlation Analysis With Application to Data from Optical Sensors
Giulia Patanè, Federica Nicolussi, Alexander Krauth +4
We address the problem of predicting a target ordinal variable based on observable features consisting of functional profiles. This problem is crucial, especially in decision-makin…
Robust functional PCA for relative data
Jeremy Oguamalam, Peter Filzmoser, Karel Hron +2
This paper introduces a robust approach to functional principal component analysis (FPCA) for relative data, particularly density functions. While recent papers have studied densit…
funcharts: Control charts for multivariate functional data in R
Christian Capezza, Fabio Centofanti, Antonio Lepore +3
Modern statistical process monitoring (SPM) applications focus on profile monitoring, i.e., the monitoring of process quality characteristics that can be modeled as profiles, also…
Robust Functional ANOVA with Application to Additive Manufacturing
Fabio Centofanti, Bianca Maria Colosimo, Marco Luigi Grasso +3
The development of data acquisition systems is facilitating the collection of data that are apt to be modelled as functional data. In some applications, the interest lies in the id…
A new class of -transformations for the spatial analysis of Compositional Data
Lucia Clarotto, Denis Allard, Alessandra Menafoglio
Georeferenced compositional data are prominent in many scientific fields and in spatial statistics. This work addresses the problem of proposing models and methods to analyze and p…
Social and material vulnerability in the face of seismic hazard: an analysis of the Italian case
Oleksandr Didkovskyi, Giovanni Azzone, Alessandra Menafoglio +1
The assessment of the vulnerability of a community endangered by seismic hazard is of paramount importance for planning a precision policy aimed at the prevention and reduction of…
Bivariate Densities in Bayes Spaces: Orthogonal Decomposition and Spline Representation
Karel Hron, Jitka Machalová, Alessandra Menafoglio
A new orthogonal decomposition for bivariate probability densities embedded in Bayes Hilbert spaces is derived. It allows one to represent a density into independent and interactiv…
Adaptive Smoothing Spline Estimator for the Function-on-Function Linear Regression Model
Fabio Centofanti, Antonio Lepore, Alessandra Menafoglio +2
In this paper, we propose an adaptive smoothing spline (AdaSS) estimator for the function-on-function linear regression model where each value of the response, at any domain point,…
A novel dowscaling procedure for compositional data in the Aitchison geometry with application to soil texture data
Federico Gatti, Alessandra Menafoglio, Niccolò Togni +4
In this work, we present a novel downscaling procedure for compositional quantities based on the Aitchison geometry. The method is able to naturally consider compositional constrai…
Changing reference measure in Bayes spaces with applications to functional data analysis
R. Talska, A. Menafoglio, K. Hron +2
Probability density functions (PDFs) can be understood as continuous compositions by the theory of Bayes spaces. The origin of a Bayes space is determined by a given reference meas…
Kriging Riemannian Data via Random Domain Decompositions
Alessandra Menafoglio, Davide Pigoli, Piercesare Secchi
Data taking value on a Riemannian manifold and observed over a complex spatial domain are becoming more frequent in applications, e.g. in environmental sciences and in geoscience.…