arXiv subjects

Domenico Perrotta

Publications and source records attributed to Domenico Perrotta.

9 recordsLinked to original sources

arXiv2026

Sentiment sensing: comparative analysis of multilingual sentiment dynamics

Cross-country sentiment analysis faces three fundamental challenges: language-specific biases distort comparisons, compositional data constraints invalidate standard statistics and temporal dynamics lack rigorous testing frameworks. We introduce sentiment sensing, a framework that addresses these gaps through integrated statistical methods. By combining compositional data analysis, functional data methods, and non-parametric inference, sentiment sensing enables valid multilingual comparisons and statistically defensible detection of sentiment shifts. Our framework has been tested in two case studies, analyzing news articles from the 27 countries of the European Union on the topics of energy and artificial intelligence. Sentiment sensing focuses on multilingual sentiment analysis enabling rigorous comparative science.

cs.CL↗

arXiv2026

Calibration of robust covariance estimation under heavy tails

The Minimum Covariance Determinant (MCD) estimator is one of the most widely used high-breakdown estimators of multivariate location and scatter. Its practical success relies on suitable corrections, which remove the asymptotic and small-sample biases introduced by trimming. While explicit consistency factors are available under the multivariate normal distribution and, more recently, under multivariate Student-t models, these corrections only address the asymptotic behavior of the estimator. In the Gaussian case, it is known showed that the MCD also exhibits a non-negligible finite-sample bias, particularly for small sample sizes and relatively large dimensions. Such bias propagates to robust Mahalanobis distances and may affect subsequent procedures, including multivariate outlier detection and robust inference. This paper investigates the finite-sample behavior of the MCD estimator and of the related robust distances under multivariate Student-t distributions, a potentially relevant scenario in many application fields. Extensive Monte Carlo experiments are carried out over a broad range of sample sizes, dimensions, trimming levels and degrees of freedom. Based on these experiments, empirical correction factors are obtained and approximated by smooth functions, providing a computationally efficient calibration suitable for practical implementations to heavy-tailed data. The proposed corrections are developed both for the scatter bias and the robust distance quantiles, thus leading to improved approximations for robust inference.

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arXiv2026

Interactive and Robust PCA Graphics with FSDA in MATLAB

Principal Component Analysis (PCA) is a standard method for dimension reduction and the exploratory analysis of multivariate data, but a complete applied workflow often requires more than numerical eigenvectors and static plots. This article documents a set of routines in the FSDA toolbox for MATLAB that implements an integrated workflow for interactive and robust PCA graphics. The software contribution consists of coordinating classical SVD-based PCA, dynamic biplot scaling controlled by row and column parameters, score-distance and orthogonal-distance diagnostics, hard-trimming robust PCA through the Minimum Covariance Determinant and the Forward Search, linked brushing, geographical displays, and three-dimensional projection diagnostics. The implementation returns reproducible numerical output while also exposing graphical controls for changing biplot geometry, confidence regions, displayed labels, robust trimming levels, and linked selections. The methodology is illustrated on quality-of-life indicators for Italian provinces, where the software reveals variable groups, atypical provinces, robust and non-robust PCA structures, and geographical patterns. The emphasis is on statistical software: no new PCA estimator is proposed, but established PCA and robust-PCA methodology is combined in a documented, extensible, and script-reproducible MATLAB environment.

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arXiv2025

Robust inference under Benford's law

We address the task of identifying anomalous observations by analyzing digits under the lens of Benford's law. Motivated by the crucial objective of providing reliable statistical analysis of customs declarations, we answer one major and still open question: How can we detect the behavior of operators who are aware of the prevalence of the Benford's pattern in the digits of regular observations and try to manipulate their data in such a way that the same pattern also holds after data fabrication? This challenge arises from the ability of highly skilled and strategically minded manipulators in key organizational positions or criminal networks to exploit statistical knowledge and evade detection. For this purpose, we write a specific contamination model for digits, obtain new relevant distributional results and derive appropriate goodness-of-fit statistics for the considered adversarial testing problem. Along our path, we also unveil the peculiar relationship between two simple conformance tests based on the distribution of the first digit. We show the empirical properties of the proposed tests through a simulation exercise and application to data from international trade transactions. Although we cannot claim that our results are able to anticipate data fabrication with certainty, they surely point to situations where more substantial controls are needed. Furthermore, our work can reinforce trust in data integrity in many critical domains where mathematically informed misconduct is suspected.

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arXiv2023

A practically efficient fixed-pivot selection algorithm and its extensible MATLAB suite

Many statistical problems and applications require repeated computation of order statistics, such as the median, but most statistical and programming environments do not offer in their main distribution linear selection algorithms. We introduce one, formally equivalent to quickselect, which keeps the position of the pivot fixed. This makes the implementation simpler and much practical compared with the best known solutions. It also enables an "oracular" pivot position option that can reduce a lot the convergence time of certain statistical applications. We have extended the algorithm to weighted percentiles such as the weighted median, applicable to data associated with varying precision measurements, image filtering, descriptive statistics like the medcouple and for combining multiple predictors in boosting algorithms. We provide the new functions in MATLAB, C and R. We have packaged them in a broad MATLAB toolbox addressing robust statistical methods, many of which can be now optimised by means of efficient (weighted) selections.

stat.ME↗

arXiv2022

Tk-merge: Computationally Efficient Robust Clustering Under General Assumptions

We address general-shaped clustering problems under very weak parametric assumptions with a two-step hybrid robust clustering algorithm based on trimmed k-means and hierarchical agglomeration. The algorithm has low computational complexity and effectively identifies the clusters also in presence of data contamination. We also present natural generalizations of the approach as well as an adaptive procedure to estimate the amount of contamination in a data-driven fashion. Our proposal outperforms state-of-the-art robust, model-based methods in our numerical simulations and real-world applications related to color quantization for image analysis, human mobility patterns based on GPS data, biomedical images of diabetic retinopathy, and functional data across weather stations.

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arXiv2021

Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection

User-generated content (e.g., tweets and profile descriptions) and shared content between users (e.g., news articles) reflect a user's online identity. This paper investigates whether correlations between user-generated and user-shared content can be leveraged for detecting disinformation in online news articles. We develop a multimodal learning algorithm for disinformation detection. The latent representations of news articles and user-generated content allow that during training the model is guided by the profile of users who prefer content similar to the news article that is evaluated, and this effect is reinforced if that content is shared among different users. By only leveraging user information during model optimization, the model does not rely on user profiling when predicting an article's veracity. The algorithm is successfully applied to three widely used neural classifiers, and results are obtained on different datasets. Visualization techniques show that the proposed model learns feature representations of unseen news articles that better discriminate between fake and real news texts.

cs.CL↗

arXiv2018

Robust Monitoring of Time Series with Application to Fraud Detection

Time series often contain outliers and level shifts or structural changes. These unexpected events are of the utmost importance in fraud detection, as they may pinpoint suspicious transactions. The presence of such unusual events can easily mislead conventional time series analysis and yield erroneous conclusions. In this paper we provide a unified framework for detecting outliers and level shifts in short time series that may have a seasonal pattern. The approach combines ideas from the FastLTS algorithm for robust regression with alternating least squares. The double wedge plot is proposed, a graphical display which indicates outliers and potential level shifts. The methodology was developed to detect potential fraud cases in time series of imports into the European Union, and is illustrated on two such series.

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arXiv2014

A Parametric Framework for the Comparison of Methods of Very Robust Regression

There are several methods for obtaining very robust estimates of regression parameters that asymptotically resist 50% of outliers in the data. Differences in the behaviour of these algorithms depend on the distance between the regression data and the outliers. We introduce a parameter $λ$ that defines a parametric path in the space of models and enables us to study, in a systematic way, the properties of estimators as the groups of data move from being far apart to close together. We examine, as a function of $λ$, the variance and squared bias of five estimators and we also consider their power when used in the detection of outliers. This systematic approach provides tools for gaining knowledge and better understanding of the properties of robust estimators.

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