Francesco Bartolucci

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Full Professor

University of Perugia

Italy

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About

Francesco Bartolucci is a Full Professor at the University of Perugia in Italy. His research areas include maximum likelihood estimation, multivariate models, and the analysis of longitudinal networks. He has published recent articles focusing on latent transition models, dynamics of global food access, and the dependencies among cryptocurrency log-returns. His work contributes significantly to the fields of statistics and econometrics.

Recent Grants

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Mixture and latent variable models for causal inference and analysis of socio-economic data

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Articles (12)

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2021

Collaborators (5)

Silvia BACCI

University of Florence

ITALY

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Antonietta Mira

SWITZERLAND

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Stefano Federici

Associate professor

Università degli Studi di Napoli Federico II

ITALY

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Ewa Genge

Assistant Professor

University of Economics in Katowice

POLAND

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Gianfranco Forte

Associate Professor

University of Milano-Bicocca

ITALY

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Francesco Bartolucci

Research Interests

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About

Recent Grants

Articles (12)

Maximum Likelihood Estimation of Multivariate Regime Switching Student‐t Copula Models

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks

Estimating the size of a closed population by modeling latent and observed heterogeneity

A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development

Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data

Testing for state dependence in the fixed-effects ordered logit model

Are attitudes toward immigration changing in Europe? An analysis based on latent class IRT models

Male recognition bias in sex assignment based on visual stimuli

Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model

A hidden Markov space–time model for mapping the dynamics of global access to food

A multivariate statistical approach to predict COVID‐19 count data with epidemiological interpretation and uncertainty quantification

Modelling Nonstationary Spatial Lag Models with Hidden Markov Random Fields

Collaborators (5)

Silvia BACCI

Antonietta Mira

Stefano Federici

Ewa Genge

Gianfranco Forte

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