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Silvia MONTAGNA
Ricercatore t.d. art. 24 c. 3 lett. B
Dipartimento di Economia "Marco Biagi"
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Pubblicazioni
2024
- Statistica: dalla datificazione dei processi alla previsione
[Capitolo/Saggio]
Cavicchioli, Maddalena; Demaria, Fabio; FRANCO VILLORIA, Maria; Frederic, Patrizio; Martini, Maria Cristiana; Montagna, Silvia; Morlini, Isabella
abstract
2023
- Bayesian GARCH modeling of functional sports data
[Articolo su rivista]
Dolmeta, P; Argiento, R; Montagna, S
abstract
The use of statistical methods in sport analytics has gained a rapidly growing interest over the last decade, and nowadays is common practice. In particular, the interest in understanding and predicting an athlete's performance throughout his/her career is motivated by the need to evaluate the efficacy of training programs, anticipate fatigue to prevent injuries and detect unexpected of disproportionate increases in performance that might be indicative of doping. Moreover, fast evolving data gathering technologies require up to date modelling techniques that adapt to the distinctive features of sports data. In this work, we propose a hierarchical Bayesian model for describing and predicting the evolution of performance over time for shot put athletes. We rely both on a smooth functional contribution and on a linear mixed effect model with heteroskedastic errors to represent the athlete-specific trajectories. The resulting model provides an accurate description of the performance trajectories and helps specifying both the intra- and inter-seasonal variability of measurements. Further, the model allows for the prediction of athletes' performance in future sport seasons. We apply our model to an extensive real world data set on performance data of professional shot put athletes recorded at elite competitions.
2023
- Clustering Athlete Performances in Track and Field Sports
[Relazione in Atti di Convegno]
Argiento, Raffaele; Colombi, Alessandro; Modotti, Lorenzo; Montagna, Silvia
abstract
This study aims to cluster track and field athletes based on their average seasonal performance. Athletes’ performance measurements are treated as random perturbations of an underlying individual step function with season-specific random intercepts. A hierarchical Dirichlet process is used as a nonparametric prior to in- duce clustering of the observations across seasons and athletes. By linking clusters across seasons, similarities and differences in performance are identified. Using a real-world longitudinal shot put data set, the method is illustrated.
2023
- Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods
[Articolo su rivista]
Berloco, C.; Argiento, R.; Montagna, S.
abstract
2023
- The hierarchical Beta-Bernoulli process as out-of-scope query detector
[Relazione in Atti di Convegno]
Dalla Pria, Marco; Montagna, Silvia
abstract
2022
- Bayesian functional mixed effects model for sports data
[Relazione in Atti di Convegno]
Dolmeta, Patric; Argiento, Raffaele; Montagna, Silvia
abstract
2021
- Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis
[Articolo su rivista]
Montagna, S.; Orani, V.; Argiento, R.
abstract
In professional tennis, it is often acknowledged that the server has an initial advantage. Indeed, the majority of points are won by the server, making the serve one of the most important elements in this sport. In this paper, we focus on the role of the serve advantage in winning a point as a function of the rally length. We propose a Bayesian isotonic logistic regression model for the probability of winning a point on serve. In particular, we decompose the logit of the probability of winning via a linear combination of B-splines basis functions, with athlete-specific basis function coefficients. Further, we ensure the serve advantage decreases with rally length by imposing constraints on the spline coefficients. We also consider the rally ability of each player, and study how the different types of court may impact on the player’s rally ability. We apply our methodology to a Grand Slam singles matches dataset.
2021
- PREDICTION OF GENE EXPRESSION FROM TRANSCRIPTION FACTORS AFFINITIES: AN APPLICATION OF BAYESIAN NON-LINEAR MODELLING
[Relazione in Atti di Convegno]
Marotta, Federico; Provero, Paolo; Montagna, Silvia
abstract
2021
- PREDICTIVE POWER OF BAYESIAN CAR MODELS ON SCALE FREE NETWORKS: AN APPLICATION FOR CREDIT RISK
[Relazione in Atti di Convegno]
Berloco, Claudia; Argiento, Raffaele; Montagna, Silvia
abstract
2020
- A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes
[Articolo su rivista]
Samartsidis, P.; Seaman, S. R.; Montagna, S.; Charlett, A.; Hickman, M.; Angelis, D. D.
abstract
A problem that is frequently encountered in many areas of scientific research is that of estimating the effect of a non-randomized binary intervention on an outcome of interest by using time series data on units that received the intervention (‘treated’) and units that did not (‘controls’). One popular estimation method in this setting is based on the factor analysis (FA) model. The FA model is fitted to the preintervention outcome data on treated units and all the outcome data on control units, and the counterfactual treatment-free post-intervention outcomes of the former are predicted from the fitted model. Intervention effects are estimated as the observed outcomes minus these predicted counterfactual outcomes. We propose a model that extends the FA model for estimating intervention effects by jointly modelling the multiple outcomes to exploit shared variability, and assuming an auto-regressive structure on factors to account for temporal correlations in the outcome. Using simulation studies, we show that the method proposed can improve the precision of the intervention effect estimates and achieve better control of the type I error rate (compared with the FA model), especially when either the number of preintervention measurements or the number of control units is small. We apply our method to estimate the effect of stricter alcohol licensing policies on alcohol-related harms.
2020
- Estimating the prevalence of missing experiments in a neuroimaging meta-analysis
[Articolo su rivista]
Samartsidis, Pantelis; Montagna, Silvia; Laird, Angela R; Fox, Peter T; Johnson, Timothy D; Nichols, Thomas E
abstract
Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple fMRI experiments with the goal of obtaining results that are more likely to generalise. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publications bias that refers to experiments that are carried out but are not published. Using foci per contrast count data from the BrainMap database, we propose a zero-truncated modelling approach that allows us to estimate the prevalence of non-significant experiments. We validate our method with simulations and real coordinate data generated from the Human Connectome Project. Application of our method to the data from BrainMap provides evidence for the existence of a file drawer effect, with the rate of missing experiments estimated as at least 6 per 100 reported. The R code that we used is available at https://osf.io/ayhfv/. This article is protected by copyright. All rights reserved.
2018
- A Bayesian Approach for the Use of Athlete Performance Data Within Anti-doping
[Articolo su rivista]
Montagna, Silvia; Hopker, James
abstract
2018
- Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data
[Articolo su rivista]
Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.
abstract
2017
- The coordinate-based meta-analysis of neuroimaging data
[Articolo su rivista]
Samartsidis, Pantelis; Montagna, Silvia; Johnson, Timothy D.; Nichols, Thomas E.
abstract
2016
- Computer Emulation with Non-stationary Gaussian Processes
[Articolo su rivista]
Montagna, Silvia; Tokdar, S. T.
abstract
2012
- Bayesian Latent Factor Regression for Functional and Longitudinal Data
[Articolo su rivista]
Montagna, Silvia; Tokdar, Surya T.; Neelon, Brian; Dunson, David B.
abstract