ENBIS-26 Conference

Session

Statistical / Stochastic Modelling and Statistical Computing

Sep 7, 2026, 10:55 AM

Conveners

Statistical / Stochastic Modelling and Statistical Computing

• Véronique Maume-Deschamps (Institut Camille Jordan, Université Claude Bernard Lyon 1)

Statistical / Stochastic Modelling and Statistical Computing

• Biagio Palumbo (Università di Napoli Federico II)

24. Least trimmed squares regression with missing values and cellwise outliers

Peter Rousseeuw (University of Leuven)

9/7/26, 10:55 AM

Statistical / Stochastic Modelling and Statistical Computing

Regression is the workhorse of statistics, and is often faced with real data that contain outliers. When these are casewise outliers, that is, cases that are entirely wrong or belong to a different population, the issue can be remedied by existing casewise robust regression methods. It is another matter when cellwise outliers occur, that is, suspicious individual entries in the data matrix...

59. Characterization of multi-way binary tables with uniform margins and fixed correlations

Fabio Rapallo (University of Genova)

9/7/26, 11:15 AM

Statistical / Stochastic Modelling and Statistical Computing

In many applied settings involving binary variables, practitioners typically rely on pairwise measures of dependence, such as correlations or agreement indices. However, when more than two variables are involved, these quantities do not uniquely determine the joint distribution. Instead, they define a family of admissible distributions that share the same pairwise structure while potentially...

85. Estimating Multivariate Generalized Gamma Convolutions via Kernel Stein Discrepancy

Léo Gonin (Institut Camille Jordan)

9/7/26, 11:35 AM

Statistical / Stochastic Modelling and Statistical Computing

This work focuses on the estimation of multivariate generalized gamma convolutions (MGGC), a class of distributions widely used in risk modeling for which no closed-form density is available. In practice, only their characteristic functions are known, which makes standard estimation methods such as maximum likelihood inapplicable. To overcome this difficulty, we adopt an RKHS-based approach...

113. Tourist Mobility: A Markov Chain Approach Using Origin--Destination Survey Data

Ms francesca atzori (university of Cagliari)

9/7/26, 12:00 PM

Statistical / Stochastic Modelling and Statistical Computing

This study develops a data-driven Markov chain framework to analyse tourist mobility patterns using empirical origin–destination data collected through surveys at a tourism information point. The dataset records both the municipality visited immediately prior to the survey and the subsequent intended destination, enabling the estimation of transition probability matrices that govern the...

114. Adaptive Particle MCMC for non-linear battery state-space models

Edvinas Juozapaitis (Kaunas University of Technology)

9/7/26, 12:20 PM

Statistical / Stochastic Modelling and Statistical Computing

State-space models have become core tools in industry as the basis of digital twin technology, enabling online system state monitoring. Advanced Bayesian methods, such as Particle Markov Chain Monte Carlo (PMCMC), may be used for state and parameter inference in non-linear state-space models. The approach combines particle filtering to approximate the hidden state posterior distribution and a...

118. Robust graphical modeling under data contamination

Christian Capezza (Department of Industrial Engineering, University of Naples "Federico II")

9/7/26, 12:40 PM

Statistical / Stochastic Modelling and Statistical Computing

High-dimensional data generated by modern multi-sensor systems call for statistical methods able to capture complex dependency structures. Graphical models are a popular tool for this purpose, as they represent conditional relationships between variables through a network. However, classical estimation techniques can be severely affected by the presence of outliers. Traditional contamination...