Mattia Stival
I am a PostDoc at the Department of Economics of the Ca' Foscari University of Venice, where I am working as a statistician on the project Age-it (Spoke 10) under the supervision of Prof. S. Campostrini.
My research interests are mainly related to the development of statistical models for the analysis of complex data, with a focus on time series data and Bayesian methods. Other than the project Age-it, I am working with wind turbine data, focusing on monitoring and forecasting wind power production, as well as on sports data, which were the topic of my PhD research.
Before coming to Venice, I was a postdoc at the Department of Statistical Sciences at the University of Padova, where I also studied for my PhD. I got my degree in May 2022, under the supervision of M. Bernardi and P. Dellaportas, with a dissertation titled "Sports performance analysis with state space models".
Timeline
[Mar. 2023-Now]
Postdoctoral Research Fellow (SECS-S/05) under the supervision of Prof. S Campostrini.
Where: Department of Economics, Ca' Foscari University of Venice.
Project: Age-it. Spoke 10. Theoretical and data-driven study of the dynamic and mechanisms generating sub-population health-inequalities in longevity and healthy ageing.
[Feb. 2022-Feb. 2023]
Postdoctoral Research Fellow (SECS-S/01) under the supervision of Prof. M. Cattelan.
Where: Department of Statistical Sciences, University of Padova.
Project: Changepoint detection in sequences of multivariate time series with application to personal healthcare monitoring.
[Oct. 2018-Dec. 2021]
PhD student under the supervision of Prof. M. Bernardi and P. Dellaportas.
Where: Department of Statistical Sciences, University of Padova.
Dissertion title: Sports performance analysis with state space models (discussed: 04/05/2022).
Main publications
Working papers and arxiv
Stival M., Schiavon L., Campostrini S. (2024). A Bayesian approach to uncover spatio-temporal determinants of heterogeneity in repeated cross-sectional health surveys. Arxiv here.
Methods
Stival M., Bernardi M., Cattelan M., Dellaportas P. (2023). Missing data patterns in runners' careers: do they matter? Journal of the Royal Statistical Society: Series C (Applied Statistics) https://doi.org/10.1093/jrsssc/qlad009.
Stival M., Bernardi M., Dellaportas P. (2023). Doubly-online changepoint detection for monitoring health status during sports activities. Annals of Applied Statistics doi.org/10.1214/22-AOAS1724.
Stival M. (2022). Sports performance analysis with state space models. PhD thesis. In 2023, the thesis received an honorable mention (menzione di merito) at the award ceremony for the best doctoral dissertation in Applied Statistics of the Italian Statistical Society (SIS).
Applications
Brustio P.R., Stival M., Boccia G. (2023). Relative age effect reversal on the junior-to-senior transition in world-class athletics. Journal of Sports Sciences. https://doi.org/10.1080/02640414.2023.2245647
Conference proceedings, posters, slides, etc.
Discussions
Stival M., Schiavon L. (2023). Mattia Stival and Lorenzo Schiavon’s contribution to the Discussion of “Flexible marked spatio-temporal point processes with applications to event sequences from association football” by Narayanan, Kosmidis and Dellaportas, Journal of the Royal Statistical Society Series C: Applied Statistics, https://doi.org/10.1093/jrsssc/qlad068.
Conference proceedings
Stival M., Bernardi M., Cattelan M., Dellaportas P. (2022). Longitudinal clustering of athletes’ careers under informative missing data patterns. In: Proceedings of the 36-th International Workshop on Statistical Modelling.
Stival M., Bernardi M., Cattelan M., Dellaportas P. (2022). Double clustering with a matrix-variate regression model: finding groups of athletes and disciplines in decathlon’s data. In: Book of Short Papers SIS 2022.
Stival M., Volpe A., Bidogia L. (2022). BASAS: a graphical tool to investigate variability, repeatability and asymmetries in Squat. In: Proceedings of the 14th Conference of the International Sports Engineering Association (ISEA2022).
Stival M. (2021). A dynamic matrix-variate model for clustering time series with multiple sources of variation. In: Book of Short Papers SIS 2021.
Stival M., Bernardi M. (2020). Dynamic Bayesian clustering of sport activities. In: Proceedings of the 35 th International Workshop on Statistical Modelling.
Stival M., Bernardi M. (2019). Dynamic Bayesian clustering of running activities. In: Smart Statistics for Smart Applications (SIS 2019).
Posters (selected)
Stival M., Bernardi M., Dellaportas P. (2022). Doubly-online changepoint detection for monitoring health status during sports activities. 800 years UNIPD.
Stival M., Bernardi M., Cattelan M., Dellaportas P. (2022). Missing data patterns in runners' careers: do they matter? Greeks Stochastics, Corfù.
Slides
Links
NEWS