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Cristiano VarinView profile

Professor

• Statistical Methodology
• Composite Likelihood Inference
• Copula Regression
• +3 more

Cristiano Varin is a Full Professor in Statistics at the Ca' Foscari University of Venice , affiliated with the Department of Environmental Sciences, Computer Science and Statistics (DAIS). He works at the Scientific Campus in via Torino and maintains the DAIS website for research and teaching updates. His research focuses on: Composite likelihood inference - A key methodological contributor with seminal papers in Biometrika and Statistica Sinica Copula regression - With practical implementations in R software Meta-analysis - Including improved likelihood inference techniques Spatial statistics - With applications to environmental data Paired comparison modeling - Applied to sports analytics and behavioral studies Recent publications demonstrate expertise in crossed random effects models (2025), ridge regression for paired comparisons (2024), and thermal comfort range analysis (2023). Scientific recognition includes: Royal Statistical Society Read Paper (2015) on journal citation modeling Gumbel Lecture (2006) by German Statistical Society Ca' Foscari Teaching Award (2020) He has collaborated with institutions including: Swiss National Science Foundation Natural Sciences and Engineering Research Council of Canada Norwegian Council of Research

• Statistical Methodology
• Machine Learning
• Parametric Inference
• +7 more

Claudia Di Caterina is an Associate Professor of Statistics at the University of Verona's Department of Economics. Her research focuses on statistical methodology, including likelihood asymptotics, pseudo-likelihoods, anytime-valid inference, and bias reduction techniques. She holds a PhD in Statistical Sciences from the University of Padova and has conducted research at University College London and the University of Toronto. She teaches courses such as Machine Learning for Economics and Statistics for Business Analysis across various programs. Her academic roles include membership in the Economics Department Council and involvement in committees like the Comitato per le Ricerche con Dati sull'Individuo (CORDI) and Commissione Paritetica Docenti-Studenti. She contributes to research laboratories like VELE (Verona Experimental Lab in Economics) and collaborates with centers such as CRIEP and CIDE. Her publications span topics in high-dimensional data analysis, confidence sequences, and causal inference. She actively engages in public events and third mission activities, including workshops on climate risk and cyber security survey methodologies.

Davide FerrariView profile

Professor

• Data Integration
• Composite Likelihood Procedures
• Model Selection
• +5 more

Davide Ferrari is a Full Professor in the Faculty of Economics and Management at the Free University of Bozen-Bolzano. He teaches statistical methods and applied statistics at undergraduate and graduate levels, and coordinates the Master's program in Data Analytics for Economics and Management. His academic career includes tenured roles as Assistant Professor at the University of Modena and Associate Professor at the University of Melbourne. Research interests include data integration, composite likelihood procedures, and high-dimensional data inference. He recently explored model selection for intractable likelihoods in econometric and environmental contexts. He actively contributes to digital education innovation through projects like EDUNEXT, focusing on AI integration in teaching methodologies. He holds a PhD from the University of Minnesota's School of Statistics (2008). Office location: BZ E2.05, Piazza Università 1, Bolzano.

Moreno BevilacquaView profile

Associate Professor

• Spatial Statistics
• Covariance Modeling
• Geostatistics
• +3 more

Moreno Bevilacqua is an Associate Professor in the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. His research focuses on spatial statistics, covariance modeling, and geostatistical methods, with applications in environmental and multivariate analysis. He has contributed to advancing covariance functions, composite likelihood approaches, and spatial data modeling techniques. Recent publications emphasize statistical methodologies for spatial and spatiotemporal data, including extensions of the Matérn model, composite likelihood for massive datasets, and copula-based approaches for bounded support data. His work bridges theoretical advancements and practical applications in environmental science and statistical computing. No scientific awards or grants are explicitly mentioned in the provided texts. Teaching activities include courses in statistics for undergraduate programs in Business Economics, covering foundational and advanced statistical methods. Current office location: Assegnisti A1 office (Zeta A building, 1st floor), Campus via Torino.

• Statistics
• Probability Theory
• Predictive Modeling
• +4 more

Federica GIUMMOLE' is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. Her research focuses on statistical prediction, probabilistic modeling, and environmental applications. She teaches courses such as Applied Statistics for Biology and Biotechnology, and Probability and Statistics across multiple programs including Engineering and Digital Management. Role: Associate Professor Department: Department of Environmental Sciences, Informatics and Statistics University: Ca' Foscari University of Venice Her research emphasizes predictive modeling, with work on statistical inference, Bayesian methods, and environmental data analysis. Recent projects include calibrating predictive distributions for meteorological forecasting and developing optimal prediction techniques for quantiles and probabilities. Publications span peer-reviewed journals like Statistical Papers and Environmental and Ecological Statistics. She has contributed to methodological advancements in scoring rules and composite likelihoods, supported by grants such as the MIUR-funded project on complex dependency models.

• Robust Statistics
• Bias Reduction Methods
• Estimation Theory
• +5 more

Nicola Lunardon is an Associate Professor of Statistics at Ca' Foscari University of Venice. He holds a PhD in Statistics from the University of Padua and has held academic positions at the University of Milan-Bicocca, University of Trieste, and others. His research focuses on robust statistics, bias reduction methods, pseudo-likelihoods, and asymptotic theory. He has contributed to advancements in estimation functions and computational statistical methodologies. Current Affiliation: Department of Environmental Sciences, Computer Science and Statistics Previous Roles: Associate Professor at University of Milan-Bicocca (2019–2023), Research Fellowships at Padua and Trieste Universities (2012–2016) Research Interests: Lunardon's work emphasizes improving inferential procedures through robust statistical techniques. He has developed methods to address bias in estimating functions, tackle incidental parameters, and enhance computational efficiency. His Bayesian non-parametric approaches have been applied to molecular systems analysis. Publications span methodological contributions in top journals like Biometrika , Journal of the Royal Statistical Society , and Biometrics . Themes include global sensitivity analysis, longitudinal data modeling, and small-sample GEE estimation. Notable Software: Co-developed the ROSE package for imbalanced learning in R