Mastodawn

Our paper on "Hellinger loss function for Generative Adversarial Networks" is posted in arXiv at http://arxiv.org/abs/2512.12267
#Statistics
#HellingerDistance
#NeuralNetworks
#GenerativeAdversarialNetworks
#RobustStatistics
#InfluenceFunction

Hellinger loss function for Generative Adversarial Networks

We propose Hellinger-type loss functions for training Generative Adversarial Networks (GANs), motivated by the boundedness, symmetry, and robustness properties of the Hellinger distance. We define an adversarial objective based on this divergence and study its statistical properties within a general parametric framework. We establish the existence, uniqueness, consistency, and joint asymptotic normality of the estimators obtained from the adversarial training procedure. In particular, we analyze the joint estimation of both generator and discriminator parameters, offering a comprehensive asymptotic characterization of the resulting estimators. We introduce two implementations of the Hellinger-type loss and we evaluate their empirical behavior in comparison with the classic (Maximum Likelihood-type) GAN loss. Through a controlled simulation study, we demonstrate that both proposed losses yield improved estimation accuracy and robustness under increasing levels of data contamination.

arXiv.org

Claudio Agostinelli Jan 22

Abstract submission and registration is open for DSSV 2026 (Trento). See https://datascience.maths.unitn.it/dssv2026/ for full information.
#DSSV2026
#daTascieNce
#StatsUnitn
#stats
#Statistics
#DataVisulization
#Conference
#Trento

Data Science, Statistics and Visualisation 2026

daTa scieNce is the web site of the students in Mathematics for daTa scieNce at the Departement of Mathematics, University of Trento

Claudio Agostinelli Jan 22

05-06 February 2026 short course by Angela Andreella on Multiple Testing and Beyond: From Error Control to Post-hoc Inference. Full information at https://datascience.maths.unitn.it/events/mt2026/index.html
#daTascieNce
#StatsUnitn
#MultipleTesting

Multiple Testing and Beyond: From Error Control to Post-hoc Inference

daTa scieNce is the web site of the students in Mathematics for daTa scieNce at the Departement of Mathematics, University of Trento

Claudio Agostinelli Jan 22

02 February 2026 seminar by Laura D'angelo on A Bayesian nonparametric approach to discriminant analysis. Full information at https://datascience.maths.unitn.it/events/bnp2026/index.html
#daTascieNce
#StatsUnitn
#BayesianNonParametric
#DiscriminantAnalysis

A Bayesian nonparametric approach to discriminant analysis

daTa scieNce is the web site of the students in Mathematics for daTa scieNce at the Departement of Mathematics, University of Trento

Claudio Agostinelli Jan 22

Our paper on "Central subspace data depth" is posted in arXiv at https://arxiv.org/abs/2601.14947
#StatisticalDataDepth
#CentralSubspace
#Symmetry
#Statistics
#DimensionReduction
#ProjectionPursuit

Central subspace data depth

Statistical data depth plays an important role in the analysis of multivariate data sets. The main outcome is a center-outward ordering of the observations that can be used both to highlight features of the underlying distribution of the data and as input to further statistical analysis. An important property of data depth is related to symmetric distributions as the point with the highest depth value, the center, coincides with the point of symmetry. However, there are applications in which it is more natural to consider symmetry with respect to a subspace of a certain dimension rather than to a point, i.e. a subspace of dimension zero. We provide a general framework to construct statistical data depths which attain maximum value in a subspace, providing a center-outward ordering from that subspace. We refer to these data depths as central subspace data depths. Moreover, if the distribution is symmetric with respect to a subspace, then the depth is maximized at that subspace. We introduce general notions of symmetry about a subspace for distributions, study the properties of central subspace data depths and provide asymptotic convergence for the corresponding sample versions. Additionally, we discuss connections with projection pursuit and dimension reduction. An application based on custom data fraud detection shows the importance of the proposed approach and strengthens its potential.

arXiv.org

Claudio Agostinelli Jan 22

Our paper on "Temporally-Evolving Generalised Networks and Their Kernels" is accepted in Statistica Sinica, see it at https://www3.stat.sinica.edu.tw/ss_newpaper/SS-2024-0345_na.pdf
or in arXiv at https://arxiv.org/abs/2309.15855
#MetricGraphs
#EuclideanGraphs
#Networks
#StochasticProcesses
#Kernels
#CovarianceMatrices
#EvolvingNetworks
#SpatialProcesses

Claudio Agostinelli Jul 8, 2025

Our paper on "Robust penalized estimators for high-dimensional
generalized linear models" is accepted in TEST, see it at https://link.springer.com/article/10.1007/s11749-025-00978-6
or in arXiv at https://arxiv.org/abs/2312.04661
#HighDimension
#GeneralizedLinearModels
#PenalizedMethods
#RobustStatistics
#MT-Estimators

Robust penalized estimators for high-dimensional generalized linear models - TEST

Robust estimators for generalized linear models (GLMs) are not easy to develop due to the nature of the distributions involved. Recently, there has been growing interest in robust estimation methods, particularly in contexts involving a potentially large number of explanatory variables. Transformed M-estimators (MT-estimators) provide a natural extension of M-estimation techniques to the GLM framework, offering robust methodologies. We propose a penalized variant of MT-estimators to address high-dimensional data scenarios. Under suitable assumptions, we demonstrate the consistency and asymptotic normality of this novel class of estimators. Our theoretical development focuses on redescending $$\rho $$ ρ -functions and penalization functions that satisfy specific regularity conditions. We present an Iterative re-weighted least-squares algorithm, together with a deterministic initialization procedure, which is crucial since the estimating equations may have multiple solutions. We evaluate the finite-sample performance of this method for Poisson distribution and well-known penalization functions through Monte Carlo simulations that consider various types of contamination, as well as an empirical application using a real dataset.

SpringerLink

Claudio Agostinelli May 17, 2025

Our paper on "A Regularized MANOVA Test for Semicontinuous High-Dimensional Data" is accepted in Biometrical Journal, see it at https://doi.org/10.1002/bimj.70054
#HighDimension
#StatisticalTest
#PermutationTest
#SemicontinuousData
#RidgePenalization

Claudio Agostinelli Mar 21, 2025

29-30 May 2025 short course by Geir Storvik on Statistical aspects to epidemiological models. Full information at
https://datascience.maths.unitn.it/events/saem2025/index.html
#daTascieNce
#StatsUnitn
#Epidemiology

Statistical aspects to epidemiological models

daTa scieNce is the web site of the students in Mathematics for daTa scieNce at the Departement of Mathematics, University of Trento

Claudio Agostinelli Mar 21, 2025

26-28 May 2025 short course by Sabrina Giordano on Hidden Markov Models for Categorical Data: Methods and Practice with R. Full information at
https://datascience.maths.unitn.it/events/hmm2025/index.html
#datTascieNce
#StatsUnitn
#HiddenMarkvoModels

Hidden Markov Models for Categorical Data: Methods and Practice with R

daTa scieNce is the web site of the students in Mathematics for daTa scieNce at the Departement of Mathematics, University of Trento