1 citations · 1 across the 9 of their papers we have counts for
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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…
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…
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…
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…