Maria FRANCO VILLORIA - personale UniMoRe

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Maria FRANCO VILLORIA

Professore Associato
Dipartimento di Economia "Marco Biagi"

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 - Informed Bayesian Finite Mixture Models via Asymmetric Dirichlet Priors [Working paper]
Page, Garritt L.; Ventrucci, Massimo; Franco-Villoria, Maria
abstract

Finite mixture models are flexible methods that are commonly used for model-based clustering. A recent focus in the model-based clustering literature is to highlight the difference between the number of components in a mixture model and the number of clusters. The number of clusters is more relevant from a practical stand point, but to date, the focus of prior distribution formulation has been on the number of components. In light of this, we develop a finite mixture methodology that permits eliciting prior information directly on the number of clusters in an intuitive way. This is done by employing an asymmetric Dirichlet distribution as a prior on the weights of a finite mixture. Further, a penalized complexity motivated prior is employed for the Dirichlet shape parameter. We illustrate the ease to which prior information can be elicited via our construction and the flexibility of the resulting induced prior on the number of clusters. We also demonstrate the utility of our approach using numerical experiments and two real world data sets.

2023 - Modelling local climate change using site-based data [Articolo su rivista]
Morlini, Isabella; Franco-Villoria, Maria; Orlandini, Stefano
abstract

2023 - STATISTICA: LA SCIENZA CHE MODELLA I DATI. Un'introduzione alle diverse tipologie di dati. [Capitolo/Saggio]
Cavicchioli, M.; Demaria, F.; FRANCO VILLORIA, M.; Frederic, P.; Morlini, I.
abstract

2022 - Che cos’è la statistica? Una prima introduzione alla scienza dei dati [Capitolo/Saggio]
Cavicchioli, Maddalena; FRANCO VILLORIA, Maria; Frederic, Patrizio; Morlini, Isabella
abstract

“Il portiere della nazionale di calcio ha parato nell’ultimo anno 6 rigori, portando a 0.75 il suo numero medio di rigori parati a partita”, “la maggioranza dei ragazzi dai 6 ai 13 anni non fa un’adeguata colazione la mattina”, “il programma trasmesso ieri in prima serata sul canale 66 del digitale terrestre ha avuto il 15% di share”, “oggi la probabilità di precipitazioni è del 60%”. Sono frasi che inevitabilmente ogni giorno ci vengono proposte nella nostra vita quotidiana. Ovunque, sempre, ci vengono presentati numeri e termini tecnici che possono sembrare strani e di difficile comprensione. Se non impariamo a trasformare le cifre che ci circondano in informazione utile, interpretata e a nostra volta comunicata in modo appropriato, non riusciremo mai a rispondere a domande importanti per le nostre decisioni e a soddisfare le nostre esigenze conoscitive. E se vi dicessimo che la statistica è proprio la materia che permette di decodificare i dati numerici e non numerici e che vi aiuta a valutare in modo intelligente le informazioni che i dati contengono? E se vi dicessimo che la statistica è fondamentale per poter sfruttare appieno le fonti di informazioni facilmente accessibili in questa era digitale? E se vi dicessimo che la statistica è ormai diventata una parte indispensabile della vita e non conoscerla e utilizzarla significa consegnare inevitabilmente il proprio futuro a qualche altra persona? Forse non avreste una preclusione a priori verso questa materia … E magari sareste anche incuriositi di sapere di cosa si occupa la statistica e quali sono le principali nozioni per iniziare a trasformare i dati in informazione utile! Questa pubblicazione, che raccoglie i contributi di docenti universitari ma che utilizza un linguaggio informale e discorsivo, cercherà di soddisfare queste vostre nuove curiosità. Il libro nasce da un’iniziativa di public engagement promossa dall’Dipartimento di Economia Marco Biagi dell’Università di Modena e Reggio Emilia (Unimore) per condividere anche con gli studenti più giovani la scoperta dei benefici e del fascino della statistica, in tutte le sue declinazioni.

2022 - How the Black Swan damages the harvest: Extreme weather events and the fragility of agriculture in development countries [Articolo su rivista]
Marmai, N.; Franco Villoria, M.; Guerzoni, M.
abstract

Climate change constitutes a rising challenge to the agricultural base of developing countries. Most of the literature has focused on the impact of changes in the means of weather variables on mean changes in production and has found very little impact of weather upon agricultural production. Instead, we focus on the relationship between extreme events in weather and extreme losses in crop production. Indeed, extreme events are of the greatest interest for scholars and policy makers only when they carry extraordinary negative effects. We build on this idea and for the first time, we adopt a conditional dependence model for multivariate extreme values to understand the impact of extreme weather on agricultural production. Specifically, we look at the probability that an extreme event drastically reduces the harvest of any of the major crops. This analysis, which is run on data for six different crops and four different weather variables in a vast array of countries in Africa, Asia and Latin America, shows that extremes in weather and yield losses of major staples are associated events. We find a high heterogeneity across both countries and crops and we are able to predict per country and per crop the risk of a yield reduction above 90% when extreme events occur. As policy implication, we can thus assess which major crop in each country is less resilient to climate shocks.

2022 - Spatial heterogeneity of Covid-19 cases in Italy [Relazione in Atti di Convegno]
Franco Villoria, M; Ventrucci, M; Rue, H
abstract

2022 - Variance partitioning in spatio-temporal disease mapping models [Articolo su rivista]
Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard
abstract

: Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov random fields, that we name the variance partitioning model. The variance partitioning model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding prior information in an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies.

2021 - Revisiting space-time disease mapping models [Relazione in Atti di Convegno]
Franco Villoria, M; Ventrucci, M; Rue, H
abstract

2019 - A unified view on Bayesian varying coefficient models [Articolo su rivista]
Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard
abstract

Varying coefficient models are useful in applications where the effect of the covariate might depend on some other covariate such as time or location. Various applications of these models often give rise to case-specific prior distributions for the parameter(s) describing how much the coefficients vary. In this work, we introduce a unified view of varying coefficients models, arguing for a way of specifying these prior distributions that are coherent across various applications, avoid overfitting and have a coherent interpretation. We do this by considering varying coefficients models as a flexible extension of the natural simpler model and capitalising on the recently proposed framework of penalized complexity (PC) priors. We illustrate our approach in two spatial examples where varying coefficient models are relevant.

2019 - Prior specification in flexible models [Relazione in Atti di Convegno]
Franco-Villoria, M; Ventrucci, M; Rue, H
abstract

2019 - Spatio-temporal analysis of extreme river flow [Relazione in Atti di Convegno]
Franco-Villoria, M; Scott, M; Hoey, H
abstract

2019 - Spatiotemporal modeling of hydrological return levels: A quantile regression approach [Articolo su rivista]
Franco-Villoria, Maria; Scott, Marian; Hoey, Trevor
abstract

2019 - Universal, Residual and External Drift Functional Kriging [Capitolo/Saggio]
Franco-Villoria, Maria; Ignaccolo, Rosaria
abstract

2019 - Variogram [Voce in Dizionario o Enciclopedia]
FRANCO VILLORIA, Maria
abstract

2018 - Bayesian varying coefficient models using PC priors [Working paper]
FRANCO VILLORIA, Maria; Massimo, Ventrucci; Håvard, Rue
abstract

Varying coefficient models arise naturally as a flexible extension of a simpler model where the effect of the covariate is constant. In this work, we present varying coefficient models in a unified way using the recently proposed framework of penalized complexity (PC) priors to build priors that allow proper shrinkage to the simpler model, avoiding overfitting. We illustrate their application in two spatial examples where varying coefficient models are relevant.

2018 - Constructing priors for varying coefficient models [Relazione in Atti di Convegno]
Franco-Villoria, M; Ventrucci, M; Rue, H
abstract

2018 - Discussion of “Using Stacking to Average Bayesian Predictive Distributions” by Yao et. al [Articolo su rivista]
Bakka, Hc; Castro-Camilo, D; Franco-Villoria, M; Freni-Sterrantino, A; Huser, Rg; Opitz, T; Rue, H
abstract

2017 - Bootstrap based uncertainty bands for prediction in functional kriging [Articolo su rivista]
FRANCO VILLORIA, Maria; Ignaccolo, Rosaria
abstract

The increasing interest in spatially correlated functional data has led to the development of appropriate geostatistical techniques that allow to predict a curve at an unmonitored location using a functional kriging with external drift model that takes into account the effect of exogenous variables (either scalar or functional). Nevertheless uncertainty evaluation for functional spatial prediction remains an open issue. We propose a semi-parametric bootstrap for spatially correlated functional data that allows to evaluate the uncertainty of a predicted curve, ensuring that the spatial dependence structure is maintained in the bootstrap samples. The performance of the proposed methodology is assessed via a simulation study. Moreover, the approach is illustrated on a well known data set of Canadian temperature and on a real data set of PM10 concentration in the Piemonte region, Italy. Based on the results it can be concluded that the method is computationally feasible and suitable for quantifying the uncertainty around a predicted curve. Supplementary material including R code is available online.

2017 - Data fusion for functional data [Relazione in Atti di Convegno]
Ignaccolo, R; Bande, S; Franco-Villoria, M; Giunta, A
abstract

2017 - Experimental Evaluation of Temperature Uncertainty Components due to Siting Condition with respect to WMO Classification [Relazione in Atti di Convegno]
Quarello, A; Ignaccolo, R; Franco-Villoria, M; Coppa, G; Merlone, A
abstract

2017 - Penalized complexity priors for varying coefficient models [Relazione in Atti di Convegno]
Franco-Villoria, M; Ventrucci, M; Rue, H
abstract

2017 - Quantile regression for functional data [Relazione in Atti di Convegno]
Franco-Villoria, M; Scott, M
abstract

2016 - A survey on ecological regression for health hazard associated with air pollution [Articolo su rivista]
Bruno, Francesca; Cameletti, Michela; Franco-Villoria, Maria; Greco, Fedele; Ignaccolo, Rosaria; Ippoliti, Luigi; Valentini, Pasquale; Ventrucci, Massimo
abstract

In the last 30 years, a large number of studies have provided substantial statistical evidence of the adverse health effects associated with air pollution. Statistical literature is very rich and includes a plethora of models to manage different types of spatial data. This paper starts with a thorough discussion on the spatial nature of the available data on health and air pollution. Health data are usually provided by Health Authorities as mortality and morbidity counts at a small area level. Thus we mainly focus on reviewing and discussing the spatial and spatio-temporal regression models proposed for disease count data on irregular lattices. In general, measuring the effect of exposure on health outcomes is an extremely hard task, and to obtain reliable estimates of the exposure effect and associated uncertainty one needs to build models that account for the residual variability not captured by the exposure–response relationship. In this context, Bayesian hierarchical models including spatial random effects play a prominent role: we consider both univariate and multivariate models and discuss some extensions to the spatio-temporal setting. Since model estimation can be prohibitive, practitioners are provided with a list of available software for Bayesian inference that avoids the need for complicated coding.

2016 - Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values [Articolo su rivista]
Franco-Villoria, Maria; Charlotte, M Wright; John, H McColl; Sherriff, Andrea; Mark, S Pearce; the Gateshead Millennium Study core team,
abstract

2016 - Bootstrap based uncertainty bands for prediction in functional kriging [Working paper]
FRANCO VILLORIA, Maria; Rosaria, Ignaccolo
abstract

2016 - Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial CO2 [Articolo su rivista]
Elayouty, Amira; Scott, Marian; Miller, Claire; Waldron, Susan; FRANCO VILLORIA, Maria
abstract

2016 - Functional Kriging Uncertainty Assessment [Relazione in Atti di Convegno]
Ignaccolo, Rosaria; Franco-Villoria, Maria
abstract

Geostatistical techniques for functional data were introduced by Goulard and Voltz (1993), but have only been developed recently. Several papers consider ordinary and universal kriging models to predict a curve at an unmonitored site under the assumption of a constant or longitude and latitude dependent mean or kriging with external drift, where scalar and functional exogenous variables are introduced. However, uncertainty evaluation of a predicted curve remains an open issue. Given the difficulty to derive sampling distributions for functional data, prediction band derivation can be approached using resampling methods. To evaluate uncertainty of a predicted curve, we adapt two semi-parametric bootstrap approach for spatially correlated data to the functional data case. The approach is illustrated by means of a simulation study.

2016 - How the Black Swan damages the harvest: statistical modelling of extreme events in weather and crop production in Africa, Asia, and Latin America [Working paper]
Marmai, N; Franco-Villoria, M; Guerzoni, M
abstract

2015 - A model for collocation uncertainty of atmospheric profiles [Relazione in Atti di Convegno]
Fasso', Alessandro; FRANCO VILLORIA, Maria; Ignaccolo, Rosaria
abstract

2015 - Collocation uncertainty in climate monitoring [Relazione in Atti di Convegno]
Franco-Villoria, M; Ignaccolo, R; Fassò, A; Madonna, F; Demoz, Bb
abstract

2015 - Modelling collocation uncertainty of 3D atmospheric profiles [Articolo su rivista]
Ignaccolo, Rosaria; FRANCO VILLORIA, Maria; Alessandro, Fassò
abstract

Atmospheric thermodynamic data are gathered by high technology remote instruments such as radiosondes, giving rise to profiles that are usually modelled as functions depending only on height. The radiosonde balloons, however, drift away in the atmosphere resulting in not necessarily vertical but three-dimensional (3D) trajectories. To model this kind of functional data, we introduce a "point based" formulation of an heteroskedastic functional regression model that includes a trivariate smooth function and results to be an extension of a previously introduced unidimensional model. Functional coefficients of both the conditional mean and variance are estimated by reformulating the model as a standard generalized additive model and subsequently as a mixed model. This reformulation leads to a double mixed model whose parameters are fitted by using an iterative algorithm that allows to adjust for heteroskedasticity. The proposed modelling approach is applied to describe collocation mismatch when we deal with couples of balloons launched at two different locations. In particular, we model collocation error of atmospheric pressure in terms of meteorological covariates and space and time mismatch. Results show that model fitting is improved once heteroskedasticity is taken into account.

2015 - Uncertainty evaluation for functional kriging: an application to the Canadian temperature data set [Relazione in Atti di Convegno]
Ignaccolo, R; Franco-Villoria, M
abstract

2014 - Kriging for functional data: uncertainty assessment [Relazione in Atti di Convegno]
Ignaccolo, Rosaria; FRANCO VILLORIA, Maria
abstract

We predict a curve at an unmonitored site taking into account exogenous variables using a functional kriging model with external drift and, alternatively, an additive model with a spatio-temporal smooth term. To evaluate uncertainty of the predicted curves, a semi-parametric bootstrap approach is used for the ﬁrst, while standard inference is used for the second. The performance of both approaches is illustrated on pollutant functional data.

2014 - Kriging uncertainty for functional data: a comparison study [Relazione in Atti di Convegno]
Franco Villoria, M; Ignaccolo, R
abstract

2014 - Spatial model for cardio-respiratory diseases hospital admission in Torino province [Relazione in Atti di Convegno]
Berchialla, P; Blangiardo, M; Cameletti, M; Finazzi, F; Franco-Villoria, M; Ignaccolo, R
abstract

2014 - Spatial modeling for air pollution epidemiology: hospital admission risk for cardio-respiratory diseases in Torino province [Relazione in Atti di Convegno]
Paola, Berchialla; Marta, Blangiardo; Michela, Cameletti; Francesco, Finazzi; FRANCO VILLORIA, Maria; Rosaria, Ignaccolo
abstract

We analyse the association between atmospheric pollution and hospital admissions for respiratory and cardiovascular causes in the province of Torino in 2004. The proposed model, which is fitted using INLA, includes fixed effects at individual and municipality level and spatially structured random effects for pollutants. Preliminary results suggests a lower risk of cardiovascular hospitalization for younger male and females, while the risk of respiratory hospitalization is significantly higher for both males and females and age classes. Summer days are associated with a lower risk for both cardiovascular and respiratory cases.

2014 - Statistical modelling of collocation uncertainty in atmospheric thermodynamic profiles [Articolo su rivista]
A., Fassò; Ignaccolo, Rosaria; F., Madonna; B. B., Demoz; FRANCO VILLORIA, Maria
abstract

The quantification of measurement uncertainty of atmospheric parameters is a key factor in assessing the uncertainty of global change estimates given by numerical prediction models. One of the critical contributions to the uncertainty budget is related to the collocation mismatch in space and time among observations made at different locations. This is particularly important for vertical atmospheric profiles obtained by radiosondes or lidar. In this paper we propose a statistical modelling approach capable of explaining the relationship between collocation uncertainty and a set of environmental factors, height and distance between imperfectly collocated trajectories. The new statistical approach is based on the heteroskedastic functional regression (HFR) model which extends the standard functional regression approach and allows a natural definition of uncertainty profiles. Along this line, a five-fold decomposition of the total collocation uncertainty is proposed, giving both a profile budget and an integrated column budget. HFR is a data-driven approach valid for any atmospheric parameter, which can be assumed smooth. It is illustrated here by means of the collocation uncertainty analysis of relative humidity from two stations involved in the GCOS reference upper-air network (GRUAN). In this case, 85% of the total collocation uncertainty is ascribed to reducible environmental error, 11% to irreducible environmental error, 3.4% to adjustable bias, 0.1% to sampling error and 0.2% to measurement error.

2014 - Uncertainty evaluation in functional kriging with external drift [Capitolo/Saggio]
FRANCO VILLORIA, Maria; Rosaria, Ignaccolo
abstract

We predict a curve at an unmonitored site taking into account exogenous variables using a functional kriging model with external drift. To evaluate uncertainty of the predicted curves, a semi-parametric bootstrap approach for spatially correlated data is adapted to functional data. Confidence bands are obtained by ordering the bootstrapped predicted curves in two different ways. The proposed approach is illustrated on pollutant functional data gathered by the monitoring network of Piemonte region (Italy).

2013 - Creation of an adiposity index for children aged 6-8 years: the Gateshead Millennium Study [Articolo su rivista]
Pearce Mark, S; James Peter, W; Franco-Villoria, Maria; Parkinson Kathryn, N; Jones Angela, R; Basterfield, Laura; Drewett Robert, F; Wright Charlotte, M; Adamson Ashley, J
abstract

A number of measures of childhood adiposity are in use, but all are relatively imprecise and prone to bias. We constructed an adiposity index (AI) using a number of different measures.

2013 - Temporal and spatial modelling of extreme river flow values in Scotland [Working paper]
FRANCO VILLORIA, Maria
abstract

Extreme river flows can lead to inundation of floodplains, with consequent impacts for society, the environment and the economy. Flood risk estimates rely on river flow records, hence a good understanding of the patterns in river flow, and, in particular, in extreme river flow, is important to improve estimation of risk. In Scotland, a number of studies suggest a West to East rainfall gradient and increased variability in rainfall and river flow. This thesis presents and develops a number of statistical methods for analysis of different aspects of extreme river flows, namely the variability, temporal trend, seasonality and spatial dependence. The methods are applied to a large data set, provided by SEPA, of daily river flow records from 119 gauging stations across Scotland. The records range in length from 10 up to 80 years and are characterized by non-stationarity and long-range dependence. Examination of non-stationarity is done using wavelets. The results revealed significant changes in the variability of the seasonal pattern over the last 40 years, with periods of high and low variability associated with flood-rich and flood-poor periods respectively. Results from a wavelet coherency analysis suggest significant influence of large scale climatic indices (NAO, AMO) on river flow. A quantile regression model is then developed based on an additive regression framework using P-splines, where the parameters are fitted via weighted least squares. The proposed model includes a trend and seasonal component, estimated using the back-fitting algorithm. Incorporation of covariates and extension to higher dimension data sets is straightforward. The model is applied to a set of eight Scottish rivers to estimate the trend and seasonality in the 95th quantile of river flow. The results suggest differences in the long term trend between the East and the West and a more variable seasonal pattern in the East. Two different approaches are then considered for modelling spatial extremes. The first approach consists of a conditional probability model and concentrates on small subsets of rivers. Then a spatial quantile regression model is developed, extending the temporal quantile model above to estimate a spatial surface using the tensor product of the marginal B-spline bases. Residual spatial correlation using a Gaussian correlation function is incorporated into standard error estimation. Results from the 95th quantile fitted for individual months suggest changes in the spatial pattern of extreme river flow over time. The extension of the spatial quantile model to build a fully spatio-temporal model is briefly outlined and the main statistical issues identified.

2012 - Extreme river flow dependence in Northern Scotland [Poster]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

2012 - Spatio-temporal quantile modelling of river flow in Scotland [Relazione in Atti di Convegno]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

2012 - Temporal investigation of flow variability in Scottish rivers using wavelet analysis [Articolo su rivista]
FRANCO VILLORIA, Maria; Scott, M; Hoey, T; Fischbacher Smith, D.
abstract

2012 - To what extent do weight gain and eating avidity during infancy predict later adiposity? [Articolo su rivista]
Wright, Charlotte M; Cox, Katherine Marie; Sherriff, Andrea; FRANCO VILLORIA, Maria; Pearce, Mark S; Adamson, Ashley J.
abstract

To determine the extent to which weight gain and eating behaviours in infancy predict later adiposity.

2011 - Conditional Probability of Flood Risk in Scotland [Relazione in Atti di Convegno]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

2011 - Spatial Analysis of Flood Risk in Scotland [Poster]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

2010 - Análisis de wavelets aplicado al estudio de la variabilidad de los ríos escoceses [Relazione in Atti di Convegno]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

2010 - Assessing the variability of Scottish rivers using wavelet analysis [Relazione in Atti di Convegno]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

2010 - Spatio-temporal study of Scottish rivers using wavelet analysis (Best Poster Presentation Award) [Poster]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

2009 - Extreme Events and business continuity planning; exploration in flood risk strategies within Scotland - Approaches to modelling long river flow time series (Best Student Presentation Award) [Relazione in Atti di Convegno]
Franco Villoria, M; Scott, M; Hoey, T; Fischbacher-Smith, D
abstract

Università degli studi di Modena e Reggio Emilia

Pubblicazioni