MARCO MINGIONE

Dottore di ricerca

ciclo: XXXIV



Titolo della tesi: On the wide applicability of Bayesian hierarchical models

This dissertation attempts to gather the main research topics I engaged during the past four years, in collaboration with several national and international researchers from “La Sapienza” and other universities. The primary focus is the application of Bayesian hierarchical models to phenomena in several domains such as economics, environmental health, and epidemiology. One common point is the attention to their fast implementation and results’ interpretability. Typically, these two main goals are challenging to be simultaneously achieved in the Bayesian setting for two main reasons: on the one hand, the fast implementation of Bayesian machineries requires an oversimplification of the modeling structure, which does not necessarily reflect the complexity of the analyzed phenomenon; on the other hand, if the estimation of complex models is sought, parameters’ interpretation may not be straightforward, especially when intricate dependence structures are present. In light of the above, all the presented applications with related solutions stemmed from these premises. The first chapter of this dissertation introduces the advantages of adopting the hierarchical paradigm for the model formulation from a conceptual perspective. Following this conceptual introduction, the second chapter delves more into the technical aspects of hierarchical model formulation and estimation. Far from being exhaustive, it provides all the essential ingredients for a thorough understanding of their theoretical foundations and optimal implementation. These first two chapters pave the road for the four original developments presented thereafter. In particular, the third chapter describes a new statistical protocol aiming at variable selection within a Beta regression model for the estimation of food losses percentages at the country-commodity level. The work has been carried out in collaboration with the Food and Agricultural Organization of the United Nations, which started in 2017 for my Master’s thesis and led to the recent publication by Mingione et al. (2021b). The fourth chapter includes an extended version of the work developed during my Visiting Research period at the University of California, Los Angeles. It describes a modeling framework for the fast estimation of temporal Gaussian processes in the presence of high-frequency biometrical sampled data. Nowadays, such data are easily collected using new non-invasive wearable devices (e.g., accelerometers) and generate substantial interest in monitoring human activity. The work is currently under review and is available in Alaimo Di Loro et al. (2021b) as a pre-print. The fifth chapter presents two modeling proposals to estimate epidemiological incidence indicators, typically collected during an epidemic for surveillance purposes. The methodology was applied to the Italian publicly available data for the monitoring of the COVID-19 epidemic. Both proposals consider probability distributions coherent with the nature of the data, which are counts, and adopt a generalized logistic function for the parametrization of the mean term. However, the second proposal allows for a latent component accounting for dependence among geographical units. Notice that, in the first work by Alaimo Di Loro et al. (2021a), the inference is pursued under a likelihood-based framework. This work helps highlighting even more the advantages of using a Bayesian approach, as subsequently described by Mingione et al. (2021a). The last chapter summarizes the main points of the dissertation, underlining the most relevant findings, the original contributions, and stressing out how Bayesian hierarchical models altogether yield a cohesive treatment of many issues.

Produzione scientifica

11573/1699823 - 2024 - Finite mixtures in capture–recapture surveys for modeling residency patterns in marine wildlife populations
Caruso, Gianmarco; Alaimo Di Loro, Pierfrancesco; Mingione, Marco; Tardella, Luca; Pace, Daniela Silvia; Jona Lasinio, Giovanna - 01a Articolo in rivista
rivista: BIOMETRICAL JOURNAL (Weinheim: Wiley-VCH, 1977-[2020] Berlin: Akad.-Verl., anfangs) pp. 1-24 - issn: 0323-3847 - wos: WOS:001142543000001 (2) - scopus: (0)

11573/1678881 - 2023 - Virtual and Reality: A Neurophysiological Pilot Study of the Sarcophagus of the Spouses
Giorgi, Andrea; Menicocci, Stefano; Forte, Maurizio; Ferrara, Vincenza; Mingione, Marco; Alaimo Di Lor, Pierfrancesco; Inguscio, Bianca Maria Serena; Ferrara, Silvia; Babiloni, Fabio; Vozzi, Alessia; Ronca, Vincenzo; Cartocci, Giulia - 01a Articolo in rivista
rivista: BRAIN SCIENCES (Basel: Molecular Diversity Preservation International) pp. 635- - issn: 2076-3425 - wos: WOS:000984056500001 (0) - scopus: 2-s2.0-85156255514 (1)

11573/1656316 - 2022 - Specification of informative priors for capture-recapture finite mixture models
Alaimo Di Loro, Pierfrancesco; Caruso, Gianmarco; Mingione, Marco; Jona Lasinio, Giovanna; Tardella, Luca - 04b Atto di convegno in volume
congresso: 51st Scientific Meeting of the Italian Statistical Society (Caserta; Italy)
libro: Book of the short papers SIS 2022 - (9788891932310)

11573/1626874 - 2022 - Decreased severity of the Omicron variant of concern: further evidence from Italy
Divino, Fabio; Alaimo Di Loro, Pierfrancesco; Farcomeni, Alessio; Jona Lasinio, Giovanna; Lovison, Gianfranco; Ciccozzi, Massimo; Mingione, Marco; Maruotti, Antonello - 01f Lettera, Nota
rivista: INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES (Decker Periodicals Publishing Incorporated:PO Box 620, LCD 1, Hamilton Ontario L8N 3K7 Canada:(800)568-7281, (905)522-7017, EMAIL: info@bcdecker.com, INTERNET: http://www.bcdecker.com, Fax: (888)311-4987 ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND, OXON, OX5 1GB) pp. - - issn: 1201-9712 - wos: WOS:000861327900004 (0) - scopus: 2-s2.0-85127712448 (1)

11573/1626885 - 2022 - Covid-19 in Italy: Modelling, communications, and collaborations
Mingione, Marco; Alaimo Di Loro, Pierfrancesco; Jona Lasinio, Giovanna; Divino, Fabio; Lovison, Gianfranco; Maruotti, Antonello; Farcomeni, Alessio - 01a Articolo in rivista
rivista: SIGNIFICANCE (-[Oxford] : Wiley-Blackwell -[Oxford] : Blackwell, on behalf of the Royal Statistical Society) pp. - - issn: 1740-9713 - wos: (0) - scopus: 2-s2.0-85127318068 (0)

11573/1652648 - 2022 - Complex Dimensionality Reduction: Ultrametric Models for Mixed-Type Data
Mingione, Marco; Vichi, Maurizio; Zaccaria, Giorgia - 02a Capitolo o Articolo
libro: SMPS 2022: Building Bridges between Soft and Statistical Methodologies for Data Science - (978-3-031-15509-3)

11573/1546415 - 2021 - Nowcasting COVID‐19 incidence indicators during the Italian first outbreak
Alaimo Di Loro, Pierfrancesco; Divino, Fabio; Farcomeni, Alessio; Jona Lasinio, Giovanna; Lovison, Gianfranco; Maruotti, Antonello; Mingione, Marco - 01a Articolo in rivista
rivista: STATISTICS IN MEDICINE (New York, NY : John Wiley & Sons) pp. 1-22 - issn: 1097-0258 - wos: WOS:000647472200001 (23) - scopus: 2-s2.0-85105234564 (25)

11573/1565763 - 2021 - Model-based clustering for estimating cetaceans site-fidelity and abundance
Caruso, Gianmarco; Panunzi, Greta; Mingione, Marco; Alaimo Di Loro, Pierfrancesco; Moro, Stefano; Bompiani, Edoardo; Lanfredi, Caterina; Pace, Daniela Silvia; Tardella, Luca; Jona Lasinio, Giovanna - 04b Atto di convegno in volume
congresso: 13th scientific meeting of the classification and data analysis group, Firenze, September 9-11, 2021 (Firenze)
libro: CLADAG 2021 book of abstracts and short papers - (978-88-5518-340-6)

11573/1580852 - 2021 - Statistical communication of COVID-19 epidemic using widely accessible interactive tools
Mingione, Marco; Alaimo Di Loro, Pierfrancesco - 04b Atto di convegno in volume
congresso: 50th Meeting of the Italian Statistical Society - SIS 2021 (Pisa; Italia (virtuale))
libro: Book of Short Papers SIS 2021 - (9788891927361)

11573/1577111 - 2021 - Spatio-temporal modelling of COVID-19 incident cases using Richards’ curve: An application to the Italian regions
Mingione, Marco; Alaimo Di Loro, Pierfrancesco; Farcomeni, Alessio; Divino, Fabio; Lovison, Gianfranco; Maruotti, Antonello; Jona Lasinio, Giovanna - 01a Articolo in rivista
rivista: SPATIAL STATISTICS (Elsevier) pp. 1-31 - issn: 2211-6753 - wos: WOS:000831532800003 (15) - scopus: 2-s2.0-85117711146 (22)

11573/1518385 - 2021 - Measuring and Modeling Food Losses
Mingione, Marco; Fabi, Carola; Jona Lasinio, Giovanna - 01a Articolo in rivista
rivista: JOURNAL OF OFFICIAL STATISTICS (Stockholm: Statistics Sweden, 1985-) pp. 171-211 - issn: 2001-7367 - wos: WOS:000669667900001 (0) - scopus: 2-s2.0-85102844778 (0)

11573/1526077 - 2021 - Capitoline dolphins. Residency patterns and abundance estimate of Tursiops truncatus at the Tiber River estuary (Mediterranean Sea)
Pace, Daniela Silvia; Di Marco, Chiara; Giacomini, Giancarlo; Ferri, Sara; Silvestri, Margherita; Papale, Elena; Casoli, Edoardo; Ventura, Daniele; Mingione, Marco; Alaimo Di Loro, Pierfrancesco; Jona Lasinio, Giovanna; Ardizzone, Domenico - 01a Articolo in rivista
rivista: BIOLOGY (Basel : MDPI) pp. 1-19 - issn: 2079-7737 - wos: WOS:000642724800001 (18) - scopus: 2-s2.0-85103840478 (19)

11573/1644910 - 2021 - Model-based clustering for monitoring cetaceans population dynamics
Panunzi, G.; Caruso, G.; Mingione, M.; Alaimo Di Loro, P.; Moro, S.; Bompiani, E.; Lanfredi, C.; Pace, D. S.; Tardella, L.; Jona Lasinio, G. - 04f Poster
congresso: GRASPA 2021 (Rome; Italy)
libro: Graspa 2021 - (979-12-200-8496-3)

11573/1471243 - 2020 - Compositional analysis of fish communities in a fast changing marine ecosystem
Mingione, M.; Alaimo Di Loro, P.; Jona Lasinio, G.; Martino, S.; Colloca, F. - 04b Atto di convegno in volume
congresso: Scientific meeting of the Italian Statistical Society (Pisa)
libro: Book of short papers - SIS 2020 - (9788891910776)

11573/1308002 - 2019 - Multivariate analysis and biodiversity partitioning of a demersal fish community. An application to Lazio coast
Mingione, Marco; Jona Lasinio, Giovanna; Martino, Sara; Colloca, Francesco - 04b Atto di convegno in volume
congresso: Smart statistics for smart applications - SIS 2019 (Milano)
libro: Smart statistics for smart applications - Book of short papers SIS2019 - (9788891915108)

11573/1308016 - 2019 - Adversarial out-domain examples for generative models
Pasquini, Dario; Mingione, Marco; Bernaschi, Massimo - 04b Atto di convegno in volume
congresso: MaL2CSec 2019 : Workshop on Machine Learning for Cyber-Crime Investigation and Cybersecurity (Stockholm; Sweden)
libro: Proceedings - 4th IEEE European Symposium on Security and Privacy Workshops, EUROS and PW 2019 - (978-1-7281-3026-2)

© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma