33 citations · 102 across the 19 of their papers we have counts for
20 papers
Cellwise, Blockwise, and Casewise Robust Multiblock PCA for Sustainable and Inclusive Wellbeing in the EU
Arthur Daisomont, Fabio Centofanti, Mia Hubert
Gross Domestic Product (GDP) is widely used to guide economic and social decision-making, but it provides only a partial view of wellbeing. For this reason, the European Union (EU)…
Cellwise Robust Discriminant Analysis
Fabio Centofanti, Can Hakan Dagidir, Mia Hubert +1
Classical discriminant analysis (DA) is based on the mean and empirical covariance matrix of each class, both of which are sensitive to outliers in the data. In the past the focus…
Cellwise and Casewise Robust Multivariate Regression with Inference
Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw
Multivariate linear regression is a fundamental statistical task, but classical estimators such as ordinary least squares are highly sensitive to outliers. These may occur as casew…
Finite-Time Observability of Oscillatory Instabilities in Synchronous p-bit Dynamics
Naoya Onizawa, Shunsuke Koshita, Takahiro Hanyu
Synchronous update schemes in p-bit annealing offer a natural route to massive parallelism, but they can also induce period-2 oscillations that degrade optimization performance. In…
Statistical Process Monitoring based on Functional Data Analysis
Fabio Centofanti
In modern industrial settings, advanced acquisition systems allow for the collection of data in the form of profiles, that is, as functional relationships linking responses to expl…
Cellwise and Casewise Robust Covariance in High Dimensions
Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw
The sample covariance matrix is a cornerstone of multivariate statistics, but it is highly sensitive to outliers. These can be casewise outliers, such as cases belonging to a diffe…
An Adaptive Multivariate Functional Control Chart
Fabio Centofanti, Antonio Lepore, Biagio Palumbo
New data acquisition technologies allow one to gather huge amounts of data that are best represented as functional data. In this setting, profile monitoring assesses the stability…
Casewise and Cellwise Robust Multilinear Principal Component Analysis
Mehdi Hirari, Fabio Centofanti, Mia Hubert +1
Multilinear Principal Component Analysis (MPCA) is an important tool for analyzing tensor data. It performs dimension reduction similar to PCA for multivariate data. However, stand…
Functional Mixture Regression Control Chart
Christian Capezza, Fabio Centofanti, Davide Forcina +2
Industrial applications often exhibit multiple in-control patterns due to varying operating conditions, which makes a single functional linear model (FLM) inadequate to capture the…
Robust Principal Components by Casewise and Cellwise Weighting
Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw
Principal component analysis (PCA) is a fundamental tool for analyzing multivariate data. Here the focus is on dimension reduction to the principal subspace, characterized by its p…
An Adaptive Multivariate Functional EWMA Control Chart
Christian Capezza, Giovanna Capizzi, Fabio Centofanti +2
In many modern industrial scenarios, the measurements of the quality characteristics of interest are often required to be represented as functional data or profiles. This motivates…
Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation
Fabio Centofanti, Mia Hubert, Biagio Palumbo +1
Multivariate Singular Spectrum Analysis (MSSA) is a powerful and widely used nonparametric method for multivariate time series, which allows the analysis of complex temporal data f…
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 Multivariate Functional Control Chart
Christian Capezza, Fabio Centofanti, Antonio Lepore +1
In modern Industry 4.0 applications, a huge amount of data is acquired during manufacturing processes that are often contaminated with anomalous observations in the form of both ca…
Real-time Monitoring of Functional Data
Fabio Centofanti, Antonio Lepore, Murat Kulahci +1
With the rise of Industry 4.0, huge amounts of data are now generated that are apt to be modelled as functional data. In this setting, standard profile monitoring methods aim to as…
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…
Sparse and Smooth Functional Data Clustering
Fabio Centofanti, Antonio Lepore, Biagio Palumbo
A new model-based procedure is developed for sparse clustering of functional data that aims to classify a sample of curves into homogeneous groups while jointly detecting the most…
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,…
Functional clustering methods for resistance spot welding process data in the automotive industry
Christian Capezza, Fabio Centofanti, Antonio Lepore +1
Quality assessment of resistance spot welding (RSW) joints of metal sheets in the automotive industry is typically based on costly and lengthy off-line tests that are unfeasible on…
Smooth Lasso Estimator for the Function-on-Function Linear Regression Model
Fabio Centofanti, Matteo Fontana, Antonio Lepore +1
A new estimator, named S-LASSO, is proposed for the coefficient function of the Function-on-Function linear regression model. The S-LASSO estimator is shown to be able to increase…