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Il presente articolo ha l’obiettivo di analizzare l’effetto della deprivazione socio-economica sull’incidenza da COVID-19 a livello sub-comunale. Grazie alla disponibilit`a di informazioni sui tassi di incidenza mensili da COVID-19 a livello di sezione di censimento per i due comuni di Palermo e Catania (Italia), viene pro- posto l’utilizzo di un modello spaziale Bayesiano con distribuzione binomiale zero- inflated. I risultati mostrano un’associazione tra livelli di deprivazione e incidenza da COVID-19 nei due comuni, controllando per la struttura spaziale delle unit`a areali considerate. Alla luce dei risultati, si rendono necessarie azioni di politica sanitaria focalizzando gli interventi su segmenti di popolazione maggiormente a rischio. This paper aims to analyse the effect of socio-economic deprivation on COVID-19 incidence at the sub-urban level. Given the availability of information on monthly incidence rates from COVID-19 at census tract level for the two munic- ipalities of Palermo and Catania (Italy), a Bayesian spatial model with zero-inflated binomial distribution is proposed. Results show an association between deprivation levels and incidence from COVID-19 in the two municipalities, also by controlling for the spatial structure of the territorial units. In the light of the results, health policy actions are needed, focusing interventions on the most deprived population groups.

We analyse the spatio-temporal distribution of visitors' stops by touristic attractions in Palermo (Italy) using theory of stochastic point processes living on linear networks. We first propose an inhomogeneous Poisson point process model, with a separable parametric spatio-temporal first-order intensity. We account for the spatial interaction among points on the given network, fitting a Gibbs point process model with mixed effects for the purely spatial component. This allows us to study first-order and second-order properties of the point pattern, accounting both for the spatio-temporal clustering and interaction and for the spatio-temporal scale at which they operate. Due to the strong degree of clustering in the data, we then formulate a more complex model, fitting a spatio-temporal Log-Gaussian Cox process to the point process on the linear network, addressing the problem of the choice of the most appropriate distance metric.

The Mediterranean International Trawl Survey (MEDITS) programme provides spatially referenced ecological data. We adopted a hierarchical Bayesian model to analyse Parapenaeus longirostris biomass data. The model comprises three parts, each of which identifies: the variability due to the explanatory variables, the variability due to the spatial domain (seen as a Gaussian Process) and the irregular component modelled as white noise. The estimated parameters show that some seabed characteristics affect biomass quantity and that the estimated behaviour of the Gaussian Process changes over different groups of years.

In mediational settings, the main focus is on the estimation of the indirect effect of an exposure on an outcome through a third variable called mediator. The traditional maximum likelihood estimation method presents several problems in the estimation of the standard error and the confidence interval of the indirect effect. In this paper, we propose a Bayesian approach to obtain the posterior distribution of the indirect effect through MCMC, in the context of mediational mixed models for longitudinal data. A simulation study shows that our method outperforms the traditional maximum likelihood approach in terms of bias and coverage rates.

clickstream data sono un’importante fonte di informazioni per l’ecommerce, sebbene non siano semplici da gestire e convertire queste informazioni in un reale vantaggio competitivo non e un compito banale. In questo articolo, consid- ` eriamo l’applicazione dei mixture hidden Markov model a dati relativi al flusso di clickstream estratti dal portale e-commerce di un’azienda di servizi turistici. Sono stati individuati cluster relativi al comportamento di navigazione degli utenti e alla loro posizione geografica che forniscono indicazioni importanti per lo sviluppo di nuove strategie di business. Clickstream data is an important source of information for businesses, however it is not easy to manage this data and also to convert the information coming out from it in competitive advantage is not a trivial task. This study considers the application of mixture hidden Markov models to clickstream data extracted from a travel services company’s e-commerce portal. We find clusters related to web users’ browsing behaviour and geographical position that provide essential indications for developing new business strategies.

Penalized inference of Gaussian graphical models is a way to assess the conditional independence structure in multivariate problems. In this setting, the conditional independence structure, corresponding to a graph, is related to the choice of the tuning parameter, which determines the model complexity or degrees of freedom. There has been little research on the degrees of freedom for penalized Gaussian graphical models. In this paper, we propose an estimator of the degrees of freedom in $$\ell _1$$ -penalized Gaussian graphical models. Specifically, we derive an estimator inspired by the generalized information criterion and propose to use this estimator as the bias term for two information criteria. We called these tuning parameter selectors GAIC and GBIC. These selectors can be used to choose the tuning parameter, i.e., the optimal tuning parameter is the minimizer of GAIC or GBIC. A simulation study shows that GAIC tends to improve the performance of both AIC-type and CV-type model selectors, in terms of estimation quality (entropy loss function) in high-dimensional setting. Moreover, GBIC model selector improves the performance of both BIC-type and CV-type model selectors, in terms of support recovery (F-score). A data analysis shows that GBIC selects a tuning parameter that produces a sparser graph with respect to BIC and a CV-type model selector (KLCV).

Global Positioning System (GPS) devices afford the opportunity to collect accurate data on unit movements from temporal and spatial perspectives. With a special focus on GPS technology in travel surveys, this paper proposes: (1) two algorithms for the pre-processing of GPS data in order to deal with outlier identification and missing data imputation; (2) a clustering approach to recover the main points of interest from GPS trajectories; and (3) a weighted-directed network, which incorporates the most relevant characteristics of the GPS trajectories at an aggregate level. A simulation study shows the goodness-of-fit of the imputation data algorithm and the robustness of the clustering algorithm. The proposed algorithms are then applied to three cases studies relating to the mobility of cruise passengers in urban contexts.

The use of network analysis to investigate social structures has recently seen a rise due to the high availability of data and the numerous insights it can provide into different fields. Most analyses focus on the topological characteristics of networks and the estimation of relationships between the nodes. We adopt a different perspective by considering the whole network as a random variable conveying the effect of an exposure on a response. This point of view represents a classical mediation setting, where the interest lies in estimating the indirect effect, that is, the effect propagated through the mediating variable. We introduce a latent space model mapping the network into a space of smaller dimension by considering the hidden positions of the units in the network. The coordinates of each node are used as mediators in the relationship between the exposure and the response. We further extend mediation analysis in the latent space framework by using Generalised Linear Models instead of linear ones, as previously done in the literature, adopting an approach based on derivatives to obtain the effects of interest. A Bayesian approach allows us to get the entire distribution of the indirect effect, generally unknown, and compute the corresponding highest density interval, which gives accurate and interpretable bounds for the mediated effect. Finally, an application to social interactions among a group of adolescents and their attitude toward substance use is presented.

This paper aims at analyzing the spatial intensity in the distribution of stop locations of cruise passengers during their visit at the destination through a stochastic point process modelling approach on a linear network. Data collected through the integration of GPS tracking technology and questionnaire-based survey on cruise passengers visiting the city of Palermo are used, to identify the main determinants which characterize their stop locations pattern. The spatial intensity of stop locations is estimated through a Gibbs point process model, taking into account for both individual-related variables, contextual-level information, and for spatial interaction among stop points. The Berman-Turner device for maximum pseudolikelihood is considered, by using a quadrature scheme generated on the network. The approach used allows taking into account the linear network determined by the street configuration of the destination under analysis. The results show an influence of both socio-demographic and trip-related characteristics on the stop location patterns, as well as the relevance of distance from the main attractions, and potential interactions among cruise passengers in stop configuration. The proposed approach represents both improvements from the methodological perspective, related to the modelling of spatial point process on a linear network, and from the applied perspective, given that better knowledge of the determinants of spatial intensity of visitors’ stop locations in urban contexts may orient destination management policy.

This paper proposes a model selection procedure to identify the number of clusters and hidden states in discrete Mixture Hidden Markov models (MHMMs). The model selection is based on a step-wise approach that uses, as score, information criteria and an entropy criterion. By means of a simulation study, we show that our procedure performs better than classical model selection methods in identifying the correct number of clusters and hidden states or an approximation of them

Il presente articolo ha lo scopo di analizzare il comportamento turistico a destinazione, con un focus specifico sulle soste effettuate dai turisti nella destinazione. Vengono analizzati dati desunti da dispositivi GPS raccolti su un campione di crocieristi, a partire dai quali e possibile individuare le soste a destinazione `attraverso l’impiego di un opportuno algoritmo. L’effetto delle caratteristiche sociodemografiche e legate all’itinerario intrapreso sul numero di soste effettuate viene studiato attraverso l’impiego di modelli di reggressione di Poisson. I risultati sono di interesse sia da un punto di vista metodologico, legato all’analisi e sintesi di dati GPS, che dal punto di vista applicato, per quanto attiene alla conoscenza del comportamento spaziale dei turisti e delle relative implicazioni per il management della destinazione. This paper aims at analysing tourist behaviour at destination by focusing on the main determinants of their stop activities. A density-based cluster algorithm identifies the stops from GPS tracking data on cruise passengers starting from data on individual trajectories. A Poisson regression model analyses the effects of socio-demographic, and itinerary characteristics on the number of stops made. The results are of interest both from a methodological perspective, related to the analysis and synthesis of GPS tracking data and from an applied perspective concerning tourists' knowledge of spatial behaviour and its implications for destination management.

This paper investigates the effect of socio-economic status on interregional mobility and mortality among cancer patients. The cohort under analysis comprises patients residing in Sicily (Italy), who were diagnosed with lung and colon cancer between 2010 and 2011. The data was collated from the hospital discharge records of the Sicilian Region and the Regional register of the causes of death, by considering all those patients for whom information relating to socio-economic status was available. First, graphical models were applied to highlight the multivariate structure of association among socio-economic status, interregional mobility and 3-year mortality. Secondly, mediation analysis quantified the direct and indirect effect of socio-economic status relating to mortality. The results revealed that socio-economic status is associated directly and indirectly with mortality due to lung cancer, mediated by interregional mobility. On the other hand, the association of socio-economic status with mortality from colon cancer is only indirect, and also mediated by interregional mobility. The authors of this research hold that the extent of patient mobility and its relation to mortality call for a reconsideration of health policy.

This paper proposes the use of Integrated Nested Laplace Approximation (Rue et al., 2009) to describe the spatial displacement of earthquake data. Specifying a hiechical structure of the data and parameters, an inhomogeneuos Log-Gaussian Cox Processes model is applied for describing seismic events occurred in Greece, an area of seismic hazard. In this way, the dependence of the spatial point process on external covariates can be taken into account, as well as the interaction among points, through the estimation of the parameters of the covariance of the Gaussian Random Field, with a computationally efficient approach.

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research.

La diffusione dei sistemi di localizzazione GPS offre numerose opportunita per la raccolta di dati di movimento. I dati GPS presentano diversi elementi di complessita derivanti anche dall’elevato dettaglio temporale e territoriale. Numerosi sono gli aspetti che possono essere presi in esame per tale tipologia di dati. Il presente studio propone un approccio statistico basato sull’identificazione dei punti di attrazione e sullo studio dei network. In particolare, viene proposto un algoritmo di identificazione di cluster, sulla base della densita di punti, che vengono sintetizzati in un network che riassume il comportamento individuale. In un secondo step, i movimenti complessivi sono aggregati ed analizzati tramite la network analysis. L’approccio proposto e applicato allo studio dei movimenti di croceristi in contesti ` urbani. The use of advanced global positional system (GPS) trackers has emerged as a novel technology in data collection of units movements. GPS data contain a large amount of information since the signals of the units are recorded almost in real time. The analysis of GPS data can be carried on several aspects of the spatial movements. In this study, we focus on statistical methods for the identification of points of interests and the analysis of the network of movements for GPS data. In particular, a density cluster-based algorithm is applied to summarize the vast amount of information and to find the most relevant points of attractions. A directed network synthesizes the individual unit path by using the latter information. Finally, we aggregate the unit paths in a weighted directed network which is studied through network analysis. We apply the proposed approach to a case study on cruise passengers’ movements in an urban context.

Global positional system data provide accurate information on units’ movements from both the temporal and the spatial perspective. Several aspects of these movements can be analyzed according to the aim of interest. In this study, we focus on statistical methods for the identification of points of interest and for the analysis of the network of movements for GPS data. A density-based cluster algorithm is applied to summarize the vast amount of information and to find the most relevant points of attraction. A directed network synthesizes the individual unit’s path by using the latter information. Finally, we aggregate the units’ paths in a weighted directed network which is studied through network analysis. The proposed approach is applied to two case studies related to cruise passengers’ movements in urban contexts.

Climate change and particularly warming are significantly impacting marine ecosystems and the services they provided. Temperature, as the main factor driving all biological processes, may influence ectotherms metabolism, thermal tolerance limits and distribution species patterns. The joining action of climate change and local stressors (including the increasing human marine use) may facilitate the spread of non-indigenous and native outbreak forming species, leading to associated economic consequences for marine coastal economies. Marine aquaculture is one among the most economic anthropogenic activities threatened by multiple stressors and in turn, by increasing hard artificial substrates at sea would facilitate the expansion of these problematic organisms and face negative consequences regarding facilities management and farmed organisms' welfare. Species Distribution Models (SDMs) are considered powerful tools for forecasting the future occurrences and distributions of problematic species used to preventively aware stakeholders. In the current study, we propose the use of combined correlative SDMs and mechanistic models, based on individual thermal performance curve models calculated through non-linear least squares regression and Bayesian statistics (functional-SDM), as an ecological relevant tool to increase our ability to investigate the potential indirect effect of climate change on the distributions of harmful species for human activities at sea, taking aquaculture as a food productive example and the benthic cnidarian Pennaria disticha (one of the most pernicious fouling species in aquaculture) as model species. Our combined approach was able to improve the prediction ability of both mechanistic and correlative models to get more ecologically informed "whole" niche of the studied species. Incorporating the mechanistic links between the organisms' functional traits and their environments into SDMs through the use of a Bayesian functional-SDM approach would be a useful and reliable tool in early warning ecological systems, risk assessment and management actions focused on important economic activities and natural ecosystems conservation.

[EN] This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor). We also use MCMC post-processor as a benchmark to make results more comparable with the proposed method. We test the ABC post-processor in two scenarios: (1) the Aipe catchment with tropical climate and a spatially-lumped hydrological model (Colombia) and (2) the Oria catchment with oceanic climate and a spatially-distributed hydrological model (Spain). The main finding of the study is that the approximate (ABC post-processor) conditional predictive uncertainty is almost equivalent to the exact predictive (MCMC post-processor) in both scenarios., This study was partially supported by the Departamento del Huila Scholarship Program No. 677 (Colombia) and Colciencias, by the Spanish Research Project TETIS-MED (ref. CGL2014-58127-C3-3-R) and TETIS-CHANGE (ref.RTI2018-093717-B-I00). Also, G. Adelfio's research has been supported by the national grant of the Italian Ministry of Education University and Research (MIUR) for the PRIN-2015 program, "Complex space-time modelling and functional analysis for probabilistic forecast of seismic events'. The authors also wish to thank the editor and the two anonymous reviewers for their thoughtful comments for the revision of the manuscript.

The purpose of this study was to determine the probability of soccer players having the best genetic background that could increase performance, evaluating the polymorphism that are considered Performance Enhancing Polymorphism (PEPs) distributed on five genes: PPAR alpha, PPARGC1A, NRF2, ACE e CKMM. Particularly, we investigated how each polymorphism works directly or through another polymorphism to distinguish elite athletes from non-athletic population. Sixty professional soccer players (age 22.5 +/- 2.2) and sixty healthy volunteers (age 21.2 +/- 2.3) were enrolled. Samples of venous blood was used to prepare genomic DNA. The polymorphic sites were scanned using PCR-RFLP protocols with different enzyme. We used a multivariate logistic regression analysis to demonstrate an association between the five PEPs and elite phenotype. We found statistical significance in NRF2 (AG/GG genotype) polymorphism/soccer players association (p < 0.05) as well as a stronger association in ACE polymorphism (p = 0.02). Particularly, we noticed that the ACE ID genotype and even more the II genotype are associated with soccer player phenotype. Although the other PEPs had no statistical significance, we proved that some of these may work indirectly, amplifying the effect of another polymorphism; for example, seems that PPAR alpha could acts on NRF2 (GG) enhancing the effect of the latter, notwithstanding it had not shown a statistical significance.In conclusion, to establish if a polymorphism can influence the performance, it is necessary to understand how they act and interact, directly and indirectly, on each other.

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithms for inference. We evaluate the computational efficiency of the proposed algorithms by an extensive simulation study and show that, when censored data are available, our proposal is superior to existing competitors both in terms of network recovery and parameter estimation. We apply the proposed method to gene expression data generated by microfluidic Reverse Transcription quantitative Polymerase Chain Reaction technology in order to make inference on the regulatory mechanisms of blood development. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/cglasso).

How a nation collects and shares data on tourism in the country is influenced by that country’s historical, cultural, and political background, as well as its geographical characteristics. This means there are a wide variety of ways tourism data are collected, evaluated, and disseminated. Although standard statistical mechanisms for tracking tourism have been improved, to be of greatest use, statistical sources should be further enhanced and be comparable across locations and time. For this reason, several international organizations have the responsibility of harmonizing definitions of, and methodologies for, collecting data. These organizations are also a relevant supplier of data sources, working to provide consistent tourism statistics.

Italian wine is increasingly appreciated in new world consumer countries and, in particular, in Russia where consumers associate its consumption with an Italian lifestyle. In this paper, market value for wine search attributes is measured through the estimation of a hedonic price model using online data from a Wine Searcher website and the information contained in the labels of wines marketed in Russia. Results show a premium price for wines from Piedmont and Tuscany, and in particular for non-native varieties and for Indicazione Geografica Tipica and Protected Geographical Indication wines. Additionally, vintage and higher alcohol content have a significant positive impact on the prices that consumers are willing to pay for wines. The knowledge of the implicit price of wine quality attributes has important implication for producers to aid in the adoption of effective marketing strategies, and for decision-makers for the adoption of effective measures to improve regional competitiveness in the world market.

This paper proposes a two-step approach for analysing the main determinants of multi-destination trip behaviour. It is based on a combination of graphical models and of a multinomial logistic regression model; the aim is to analyse direct and indirect effects of a wide set of tourist- and trip-related characteristics on multi-destination trip behaviour. Empirical data have been derived from a frontier survey of approximately 4000 incoming tourists in Sicily (Italy) at the end of their trip. Results suggest that multi-destination trips depend directly on the length of stay, the number of previous visits and motivation for the trip, and only indirectly on the interview month, travel party, organization of the trip, and country of origin. The role of other socio-demographic characteristics, such as age or gender is confirmed to be marginal or even irrelevant.

This paper introduces the use of scad-elastic net in the assessment of the determinants of individual tourist spending. This technique approaches two main estimation-related issues of primary importance. So far studies of tourism literature have made a wide use of classic regressions, whose results might be affected by multicollinearity. In addition, because of the absence of robust economic theory on tourism behavior, regressor selection is often left to researcher's choice when not driven by non-optimal automatic criteria. Scad-elastic net is an OLS model that accounts for both these problems by including two types of parameters constraints, namely the smoothly clipped absolute deviation (scad) and the ?2-norm. We analyze an official dataset of incoming tourists to Uruguay. Socio-demographic, psychographic and trip-related variables are used as explanatory of per capita per day tourist expenditure. Significant impact on tourism expenditure of some accommodation facilities such as expensive one and second dwellings, personal experiences rather than the number of past visits, doing certain activities, place of stay, and seasonality are the main conclusions that are drawn from the analysis. We use scad-elastic net to analyze tourist expenditure in Uruguay.Our penalized regression approaches overcome some limitations of classic models.The technique handles multicorrelation and regressor selection.Accommodation is crucial to predict levels of spending.Space and seasonality effect also emerge.

Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l 1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.

This paper provides evidence on the relationship within a set of financial and operational indicators for Italian airports over 2008–2014. The limited sample size of national and regional airports suggests to apply the penalised RCON ( V , E ) model, which falls within the class of Gaussian graphical models. It provides both estimate and easy way to visualise conditional independence structures of the variables. Moreover, it is particularly suitable for handling longitudinal data where small number of units and huge number of variables have been collected. Findings highlight that a qualified concept of size matters in determining good financial performance. Specifically, increasing jointly the number of movements with flights that would attract a high number of passengers may improve both sales profitability and revenues generated by the company's assets. Results suggests that the effect of low cost carrier has been heterogeneous throughout the sample, which may suggest new opportunities to expand the business in order to intercept the consumer surplus of this category of travellers.

The research focus of the present paper was twofold. First, we tried to document that human intake of trace elements is influenced by geological factors of the place of residence. Second, we showed that the elemental composition of human hair is a useful screening tool for assessing people's exposure to potentially toxic substances. For this purpose, we used samples of human hair from adolescents and applied two robust statistical approaches. Samples from two distinct geological and environmental sites were collected: the first one was characterized by the presence of the active volcano Mt. Etna (ETNA group) and the second one lithologically made up of sedimentary rocks (SIC group). Chemical data were statistically processed by Penalized Linear Discriminant Analysis (pLDA) and Discrete AdaBoost (DAB). The separation between the two groups turned out well, with few overlaps accounting for less than 5%. The chemical variables that better distinguished ETNA group from SIC group were As, Cd, Co, Li, Mo, Rb, Sr, U and V. Both pLDA and DAB allowed us to characterize the elements most closely related to the volcanic contribution (As, U and V) and those (Cd, Co, Li, Mo, Rb and Sr) prevalently influenced by the geology of the area where SIC samples were collected. We conclude that the geological characteristics of the area of residence constitute a key factor in influencing the potential exposure to trace elements. Hair analysis coupled with robust statistical methods can be effectively used as a screening procedure to identify areas at great environmental risk.

We present two methodologies to deal with high-dimensional data with mixed variables, the strongly decomposable graphical model and the regression-type graphical model. The first model is used to infer conditional independence graphs. The latter model is applied to compute the relative importance or contribution of each predictor to the response variables. Recently, penalized likelihood approaches have also been proposed to estimate graph structures. In a simulation study, we compare the performance of the strongly decomposable graphical model and the graphical lasso in terms of graph recovering. Five different graph structures are used to simulate the data: the banded graph, the cluster graph, the random graph, the hub graph and the scale-free graph. We assume the graphs are sparse. Our finding, in the simulation study, is that the strongly decomposable graphical model shows, generally, comparable or better performance both in low and high-dimensional case. Finally, we show an application on mixed data.

Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molec- ular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Penalized Gaussian graphical models have been used to estimate sparse networks. However, the literature has focussed on static networks, which lack specific temporal interpretations. We propose a flexible collection of ANOVA-like dynamic network models, where the user can select specific time dynamics, known presence or absence of links and a particular autoregressive structure. We use undirected graphical models with block equality constraints on the parameters. This reduces the number of parameters, increases the accuracy of the estimates and makes interpretation of the results more relevant. We show that the constrained likelihood optimization problem can be solved by taking advantage of an efficient solver, LogdetPPA, developed in convex optimization. Model selection strategies can be used to select a particular model. We illustrate the flexibility of the method on both synthetic and real data.

Inflammatory bowel disease (IBD) consists of two distinct clinical forms, ulcerative colitis (UC) and Crohn's disease (CD), with unknown aetiology, which nevertheless are considered to share almost identical pathophysiological backgrounds. Up to date, a full coherent mechanistic explanation for IBD is still lacking, but people start to realize that the pathogenesis of IBD involves four fundamental components: the environment, gut microbiota, the immune system and the genome. As a consequence, IBD development might be due to an altered immune response and a disrupted mechanism of host tolerance to the non-pathogenic resident microbiota, leading to an elevated inflammatory response. Considering the available data arising from the scientific literature, here reviewed, in CD, a benefit of probiotics remains unproven; in UC, a benefit of probiotics remains unproven, even if E. coli Nissle 1917 seems promising in maintaining remission and it could be considered an alternative in patients intolerant or resistant to 5-ASA preparations; in pouchitis, small controlled trials suggest a benefit from VSL no. 3 in the primary and secondary prevention of pouchitis; in IBD-associated conditions, a benefit of probiotics remains unproven. However, well-designed randomized control clinical trials are necessary to understand the undoubted role of these agents in the management of gut physiology in health and disease.

This work aims at analyzing the spatio-temporal intensity in the distribution of stop locations of cruise passengers during their visit at the destination. Data are collected through the integration of GPS tracking technology and questionnaire-based survey on a sample of cruise passengers visiting the city of Palermo (Italy), and they are used to identify the main determinants which characterize cruise passengers’ stop locations pattern. The spatio-temporal distribution of visitors' stops is analysed by mean of the theory of stochastic point processes occurring on linear networks, in order to consider the street configuration of the city and the location of the main attractions. First, an inhomogeneous Poisson point process model is proposed, with a separable parametric spatio-temporal first-order intensity. The spatial interaction among stop points on the given network is considered, by fitting a Gibbs point process model with mixed effects for the purely spatial component. This allows to study first-order and second-order properties of the point pattern, accounting both for the spatio-temporal clustering and interaction, and for the spatio-temporal scale at which they operate. Secondly, due to the strong degree of clustering in the data, a more complex model is proposed, by fitting a spatio-temporal Log-Gaussian Cox Process to the point process on the linear network, addressing the problem of the choice of the most appropriate distance metric. The proposed methodology allows considering the linear network determined by the street configuration of the destination under analysis, as well as the temporal component. The results show an influence of the distance from the main attractions, and potential interactions among cruise passengers in stop configuration. The proposed approach represents both improvements from the methodological perspective, related to the modelling of spatio-temporal point process on a linear network, and from the applied perspective, given that a better knowledge of the determinants of spatial intensity of visitors’ stop locations in urban contexts may orient destination management policy.

Ocean warming is a worldwide phenomenon. The mean temperature of the catch (MTC) is becoming one of the leading indicators to assess the impact of sea surface temperature on fish communities. In this study, we apply a semiparametric regression approach to the MTC of the catches from MEDITS bottom trawl program in the Strait of Sicily (Central Mediterranean Sea) for the period 1995 to 2018 to evaluate the effects of climate change on continental shelf fish community. All covariates included in the model have a significant impact on the MTC level. Notably, the sea surface temperature (SST) effect on the MTC depends on depth, being positive near the surface and negative at the bottom.

The use of network analysis for the investigation of social structures has recently seen a rise, due both to the high availability of data and to the numerous insights it can provide into different fields. Most analyses focus on the topological characteristics of networks and the estimation of relationships between the nodes. We adopt a different point of view, by considering the whole network as a random variable conveying the effect of an exposure on a response. This point of view represents a classical mediation setting, where the interest lies in the estimation of the indirect effect, that is, the effect propagated through the mediating variable. We introduce a latent space model mapping the network into a space of smaller dimension by considering the hidden positions of the units in the network. Furthermore, the mediation analysis is extended by using generalised linear models. A Bayesian approach allows to obtain the entire distribution of the indirect effect, generally unknown, and to compute highest density intervals, which give accurate and interpretable bounds for the mediated effect. Finally, an application to social interactions among a group of adolescents and their attitude toward smoking is presented.

We study the problem of selection of regularization parameter in penalized Gaussian graphical models. When the goal is to obtain the model with good predicting power, cross validation is the gold standard. We present a new estimator of Kullback-Leibler loss in Gaussian Graphical model which provides a computationally fast alternative to cross-validation. The estimator is obtained by approximating leave-one-out-cross validation. Our approach is demonstrated on simulated data sets for various types of graphs. The proposed formula exhibits superior performance, especially in the typical small sample size scenario, compared to other available alternatives to cross validation, such as Akaike's information criterion and Generalized approximate cross validation. We also show that the estimator can be used to improve the performance of the BIC when the sample size is small., Comment: 16 pages, 5 figures

We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood of structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The model is defined on the basis of partial correlations, which results in a specific class precision matrices. A priori L1 penalized maximum likelihood estimation in this class is extremely difficult, because of the above mentioned constraints, the computational complexity of the L1 constraint on the side of the usual positive-definite constraint. The implementation is non-trivial, but we show that the com- putation can be done effectively by taking advantage of an efficient maximum determinant algorithm (SDPT3) developed in convex optimization. For selecting the tuning parameter, we compare several selection criteria and argue that the traditional AIC and BIC should not expect to work. We compare our method with related methods, such as glasso (Friedman et al. 2007).

Dynamic gene-regulatory networks are complex since the interaction patterns between its components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between the random variables. By interpreting the random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying networks. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slowly changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course genomic data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with l1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a the heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset.