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SUMMARY:An approach to structural equation modeling in a multiblock framew
 ork
DTSTART;VALUE=DATE-TIME:20230630T111000Z
DTEND;VALUE=DATE-TIME:20230630T113000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1035@cern.ch
DESCRIPTION:Speakers: Rosaria Romano (University of Naples Federico II)\nI
 n many application fields\, the variables used to measure a phenomenon are
  gathered into homogeneous blocks that measure partial aspects of the phen
 omenon. For example\, in sensory analysis\, the overall quality of product
 s may depend on the taste and odor variables\, etc. In consumer analysis\,
  consumer preferences may depend on physical-chemical and sensory variable
 s. In some contexts\, a structure of relations between the different block
 s may exist that gives rise to a chain of influences. Within each link\, t
 he blocks of predictor variables are called input blocks\, while the block
  of dependent variables is called the output block. If the input blocks do
  not depend on any other block\, then they are defined as exogenous blocks
 \, while those that rely on other input blocks in the same relation are ca
 lled intermediate blocks. If there is a chain of the relationship between 
 the blocks\, we are then dealing with what is often called a mediation mod
 el and must interpret both indirect and direct effects among blocks.\nWith
 in the scope of multiblock data analysis with a directional path among the
  blocks\, we will present a new approach named SO‐PLS path modelling (SO
 ‐PLS‐PM). \nThe approach splits the estimation into separate sequentia
 l orthogonalized PLS regressions (SO-PLS) for each output block. The new m
 ethod is flexible and graphically oriented and allows for handling multidi
 mensional blocks and diagnosing missing paths. New definitions of total\, 
 direct\, indirect\, and additional effects in terms of explained variances
  will be proposed\, along with new methods for graphical representation. \
 nIn this research\, some interesting properties of the method will be show
 n both on simulated and real data. The actual data concerns consumer\, sen
 sory and process modelling data. Results will also be compared to those of
  alternative path modelling methods.\n\nKeywords: path analysis\, graphica
 l modelling\, multiblock regression\n\n\nReferences\nR. Romano\, O. Tomic\
 , K.H. Liland\, A. Smilde\, T. Næs (2019). A comparison of two PLS‐base
 d approaches to structural equation modeling. Journal of Chemometrics\, 33
  (3)\, e3105.\nT. Næs\, R. Romano\, O. Tomic\, I. Måge\, A. Smilde\, K.H
 . Liland. Sequential and orthogonalized PLS (SO‐PLS) regression for path
  analysis: Order of blocks and relations between effects. Journal of Chemo
 metrics\, 35 (10)\, e3243.\n\nhttps://indico.unina.it/event/67/contributio
 ns/1035/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1035/
END:VEVENT
BEGIN:VEVENT
SUMMARY:MCMC or Reservoir computing? A direct sampling approach
DTSTART;VALUE=DATE-TIME:20230630T090000Z
DTEND;VALUE=DATE-TIME:20230630T094000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1002@cern.ch
DESCRIPTION:Speakers: Petros Dellaportas (AUEB and UCL)\nAssume that we wo
 uld like to estimate the expected value of a function f with respect to a 
 density π by using an importance density function q. We prove that if π 
 and q are close enough under KL divergence\, an independent Metropolis sam
 pler estimator that obtains samplers from π with proposal density q\, enr
 iched with a variance reduction computational strategy based on control va
 riates\, achieves smaller asymptotic variance than the one from importance
  sampling. We illustrate our results in challenging option pricing problem
 s that require Monte Carlo estimation. Furthermore\, we propose an automat
 ic sampling methodology based on adaptive independent Metropolis that can 
 successfully reduce the asymptotic variance of an importance sampling esti
 mator and we demonstrate its applicability in a Bayesian inference problem
 s.\n\nhttps://indico.unina.it/event/67/contributions/1002/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1002/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sparse Hierarchical Vector Autoregression for Psychopathological N
 etwork Estimation from Intensive Longitudinal Data
DTSTART;VALUE=DATE-TIME:20230630T083000Z
DTEND;VALUE=DATE-TIME:20230630T085000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1013@cern.ch
DESCRIPTION:Speakers: Spyros Balafas (Vita-Salute San Raffaele University)
 \nThe use of networks as a tool for studying complex\nsystems gained popul
 arity in various scientific disciplines. In the past decade\, the ``networ
 k takeover'' reached psychology\, and networks were utilized to abstract c
 omplex psychological phenomena. In psychopathology\, a network-based frame
 work known as the *network theory of mental illness*\, posits that mental 
 disorders emerge as systems of causally interacting psychopathological sym
 ptoms. According to this framework\, symptoms and other psychological or s
 ociological factors are nodes in a psychopathological network\, and the ab
 sence of an edge between two nodes corresponds to a conditional independen
 ce relationship. In contrast to other types of networks (e.g.\, social net
 works) where the structure is observed\, in networks from psychopathology 
 the dependence structure between the nodes is not known a priori and needs
  to be estimated from data. \n\nTypically\, after estimating a psychologic
 al network\, summary statistics are used to describe its structural proper
 ties both at the global and local levels. In the psychological literature\
 , clinical outcomes such as illness severity have been associated with net
 work summary statistics such as network density. The aim of this study is 
 to test i) whether the network density differs across populations of incre
 asing illness severity\, and ii) whether local network statistics can be u
 sed to identify symptoms that are associated with illness severity. For th
 is purpose\, we use intensive longitudinal data from a $90$-day diary stud
 y called Mapping Individual Routes of Risk and Resilience (MIRORR). The da
 ta consists of $8640$ observations within $N = 96$ individuals\, divided o
 ver four subgroups representing different early clinical stages ($n_1 = 25
 $\, $n_2 = 27$\, $n_3 = 24$\, $n_4 = 20$). Participants in the lowest risk
  group were randomly selected from the general population in the north of 
 the Netherlands based on their score on the Community Assessment of Psychi
 c Experiences (CAPE) test. Inclusion criteria for the study were: aged bet
 ween $18$ and $35$ years\, reading and speaking Dutch fluently\, being cap
 able of following the research procedures\, provide informed consent. Excl
 usion criteria for participating in the study were: psychotic episodes (cu
 rrent or in the past) according to the Diagnostic and Statistical Manual o
 f Mental Disorders 4 (DSM-4)\, hearing or visual problems\, and pregnancy.
  Participants were excluded from the study when they missed more than $22$
  measurements in total or missed five or more measurements in a row. Items
  in the diary assessment covered a wide range of feelings and (subclinical
 ) psychotic experiences\, depression\, anxiety\, mania\, obsessive-compuls
 ive behavior\, and anger. Participants were required to complete a digital
  questionnaire on psychopathological symptoms\, emotions\, functioning\, a
 nd stress once a day for $90$ consecutive days. \n\nFor estimating the net
 work structure for each group of participants\, we propose a hierarchical 
 extension of the graphical vector autoregressive (GVAR) model that aims to
  model the heterogeneity in intensive longitudinal data. The parameters of
  the proposed hierarchical GVAR model are estimated within a two-step proc
 edure that combines penalized linear mixed models with graphical LASSO (gL
 ASSO). The estimated networks are then used to calculate global and local 
 network statistics\, which are compared across groups using statistical te
 sts. \n\nOur results showed that global network statistics such as network
  density and connectivity do not significantly differ as mental illness be
 comes more severe. However\, we propose the use of local network character
 istics such as centrality indices to identify emotions that correlate sign
 ificantly with increasing illness severity.\n\nhttps://indico.unina.it/eve
 nt/67/contributions/1013/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1013/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sparse Pairwise Likelihood Inference for Multivariate Time Series 
 Models
DTSTART;VALUE=DATE-TIME:20230630T073000Z
DTEND;VALUE=DATE-TIME:20230630T075000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1003@cern.ch
DESCRIPTION:Speakers: Xanthi Pedeli (Athens University of Economics and Bu
 siness\, Department of Statistics)\nMultivariate time series data is becom
 ing an increasingly common research topic. Unlike univariate time series\,
  the temporal dependence of a multivariate series includes both serial dep
 endences and interdependences across different marginal series. Consequent
 ly\, as the number of component series increases\, multivariate time serie
 s models become overparameterized. In addition\, there are many cases wher
 e the conditional distribution of the multivariate series given its past m
 ight have a complicated form. Given these challenges we develop methodolog
 y by replacing the full likelihood function by a pairwise likelihood that 
 only requires the specification of bivariate marginals instead of the mult
 ivariate distribution. Clearly\, the computational task of maximization of
  the pairwise likelihood is much simpler than maximization of the full lik
 elihood function but still it poses the problem of combining all estimator
 s. For this purpose\, we rely on maximization of an approximate weighted l
 east squares estimation criterion subject to a shrinkage penalty that allo
 ws for model selection. The suggested approach provides a general framewor
 k for multidimensional time series since it can be applied to both continu
 ous and discrete time series but also to mixed mode time series data.\n\nh
 ttps://indico.unina.it/event/67/contributions/1003/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1003/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Semiparametric regression for competing risks data with missing no
 t at random cause of failure
DTSTART;VALUE=DATE-TIME:20230629T132000Z
DTEND;VALUE=DATE-TIME:20230629T134000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1024@cern.ch
DESCRIPTION:Speakers: Giorgos Bakoyannis (Athens University of Economics a
 nd Business)\nThe cause of failure in cohort studies that involve competin
 g risks is frequently incompletely observed. Failure to deal with this iss
 ue can lead to substantially biased estimates. To the best of our knowledg
 e\, all the methods that have addressed the issue in the context of semipa
 rametric competing risks models rely on a missing at random (MAR) assumpti
 on. Nevertheless\, the MAR assumption is not realistic in many real-world 
 settings. In this work we relax the latter assumption by allowing for a cl
 ass of missing not at random (MNAR) mechanisms\, which contain the MAR mec
 hanism as a special case. Due to the inherent non-identifiability issues u
 nder MNAR\, we propose an approach for hypothesis testing that does not re
 quire the estimation of the non-estimable parameters. Using modern empiric
 al process theory\, we show that the proposed estimators are uniformly con
 sistent under the assumed class of MNAR mechanisms. We also show that our 
 estimators converge weakly to tight zero mean Gaussian processes and propo
 se rigorous methodology for the computation of confidence intervals which 
 achieve a coverage rate of at least 100*(1 - α)%\, asymptotically\, for t
 he true unknown parameters of interest. The proposed methodology is applie
 d to competing risks data from a large multicenter HIV study in sub-Sahara
 n Africa where a substantial portion of causes of failure is missing not a
 t random.\n\nhttps://indico.unina.it/event/67/contributions/1024/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1024/
END:VEVENT
BEGIN:VEVENT
SUMMARY:On the predictability of a class of ordinal data models
DTSTART;VALUE=DATE-TIME:20230629T140000Z
DTEND;VALUE=DATE-TIME:20230629T142000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1008@cern.ch
DESCRIPTION:Speakers: Rosaria Simone (University of Naples Federico II)\nT
 he contribution aims at discussing some preliminary results on the evaluat
 ion of prediction performance for the class of mixture models with uncerta
 inty (Piccolo and Simone\, 2019). The ultimate goal of the analysis is the
  evaluation of the extent by which the uncertainty specification constitut
 es an added value for prediction of ordinal scores. A small simulation stu
 dy is presented to assess prediction performance of competing models under
  miss-specification. The Ranked Probability Score is chosen as scoring rul
 e since it is the most suited to deal with ordinal data\, without the assi
 gnment of numerical scores to category.  Finally\, a variable selection pr
 ocedure based on prediction performance can be outlined on a case study fo
 r the prediction of subjective probability to survive. Comparisons with cu
 mulative link models are illustrated for the sake of completeness. Prelimi
 nary findings discussed in Simone and Piccolo (2022) indicate that uncerta
 inty modelling improves prediction performance substantially. Hence\, it i
 s important to assess the information quality of the baseline preference m
 odel (the Binomial\, for instance). To this aim\, we introduce a new utili
 ty measure for preference models when contaminated with alternative uncert
 ainty specifications in the sense proper to the framework of Information Q
 uality. As a result\, the mixing weight of the chosen feeling component wi
 thin the mixture can be explicitly interpreted in terms of model predictiv
 e ability.\n\n**Keywords**: CUB models\; Predictability\; Ranked Probabili
 ty Score\; Ordinal Data\n\n**References**:\nD. Piccolo\, R. Simone (2019).
  The class of CUB models: statistical foundations\, inferential issues and
  empirical evidence. STATISTICAL METHOD AND APPLICATIONS\, Volume 28\, pag
 es 389-435.\nR. Simone and D. Piccolo (2022). On the predictability of a c
 lass of ordinal data models. In A. Balzanella\, M. Bini\, C. Cavicchia\, a
 nd R. Verde\, editors\, Book of short papers SIS 2022\, 51st Scientific Me
 eting of the Italian Statistical Society\, pages 1053–1058. Pearson.\n\n
 https://indico.unina.it/event/67/contributions/1008/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1008/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Maximum likelihood estimation of multivariate regime switching Stu
 dent-t copula models
DTSTART;VALUE=DATE-TIME:20230629T105000Z
DTEND;VALUE=DATE-TIME:20230629T111000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1025@cern.ch
DESCRIPTION:Speakers: Federico Cortese (Università degli studi di Milano-
 Bicocca)\nWe propose a novel estimation method for multivariate regime swi
 tching models based on a Student-t copula function. These models account f
 or the interdependencies between multiple variables by considering the cor
 relation strength controlled by specific parameters. Moreover\, they addre
 ss fat-tailed distributions through the number of degrees of freedom. Thes
 e parameters\, in turn\, are governed by a latent Markov process.\n\nWe co
 nsider a two-steps procedure carried out through the Expectation-Maximizat
 ion algorithm to estimate model parameters by maximum likelihood. The prim
 ary computational challenge lies in estimating both the matrix of dependen
 ce parameters and determining the number of degrees of freedom for the Stu
 dent t-copula. To address this\, we introduce a new approach that leverage
 s Lagrange multipliers\, simplifying the estimation process.\n\nThrough a 
 comprehensive simulation study\, we demonstrate that our estimators posses
 s desirable properties in finite samples. Additionally\, the estimation pr
 ocedure shows good computational efficiency.\n\nWe apply our model to anal
 yze the log-returns of five different cryptocurrencies. The results enable
  us to identify distinct bull and bear market periods based on the intensi
 ty of correlations observed between the crypto assets. This finding highli
 ghts the model's efficacy in capturing and characterizing market dynamics 
 within the cryptocurrency domain.\n\nKeywords: statistical models for fina
 ncial analysis\, cryptocurrencies\, time series\, Expectation-Maximization
  algorithm\, latent variable models\n\nhttps://indico.unina.it/event/67/co
 ntributions/1025/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1025/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Capturing Correlated Clusters Using Mixtures of Latent Class Model
 s
DTSTART;VALUE=DATE-TIME:20230629T113000Z
DTEND;VALUE=DATE-TIME:20230629T115000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1020@cern.ch
DESCRIPTION:Speakers: Gertraud Malsiner-Walli (Vienna University of  Econo
 mics and Business)\nLatent class models rely on the conditional\n	independ
 ence assumption\, i.e.\, it is assumed that the categorical\n	variables ar
 e independent given the cluster memberships. \n	Within the Bayesian framew
 ork\, we propose a suitable specification of\n	priors for the latent class
  model to identify the clusters in\n	multivariate categorical data where t
 he independence assumption is not\n	fulfilled.  Each cluster distribution 
 is approximated by a latent\n	class model\, leading overall to a mixture o
 f latent class models.\n	The Bayesian approach allows to identify the clus
 ters and fit their\n	cluster distributions using a one-step procedure. We 
 provide suitable estimation and inference methods for the\n	mixture of lat
 ent class models and illustrate the performance of this\n	approach on arti
 ficial and real data.\n\nKeywords: Bayesian inference\, model-based cluste
 ring\, prior on the number of components\, telescoping sampler.\n\nFop\, M
 .\, K. M. Smart\, and T. B. Murphy (2017). Variable selection for latent\n
 class analysis with application to low back pain diagnosis. The Annals of\
 nApplied Statistics 11 (4)\, 2080-2110.\n\nFruehwirth-Schnatter\, S.\, G. 
 Malsiner-Walli\, and B. Gruen (2021). Generalized\nmixtures of finite mixt
 ures and telescoping sampling. Bayesian\nAnalysis 16 (4)\, 1279–1307.\n\
 nMalsiner-Walli\, G.\, S. Fruehwirth-Schnatter\, and B. Gruen (2017). Iden
 tifying\nmixtures of mixtures using Bayesian estimation. Journal of Comput
 ational\nand Graphical Statistics 26 (2)\, 285–295.\n\nhttps://indico.un
 ina.it/event/67/contributions/1020/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1020/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Latent Feeling and Uncertainty of Perception and Expectations of P
 rice levels over time: A Change Point Analysis
DTSTART;VALUE=DATE-TIME:20230629T111000Z
DTEND;VALUE=DATE-TIME:20230629T113000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1011@cern.ch
DESCRIPTION:Speakers: Carmela  Cappelli (University of Naples Federico II)
 \nFor the analysis of ordered categorical data\, CUB modelling approach en
 tails the estimation of two main structural latent components of the ratin
 g process: feeling and uncertainty\, parameterized within a two-component 
 mixture of Binomial and uniform distributions: see Piccolo and Simone 2019
  for an overview. Featuring parameters can be possibly linked to subject c
 ovariates to determine twofold response patterns and they can be promptly 
 estimated using the EM algorithm (as implemented in the R package ‘CUB
 ’ available on CRAN).\nThe contribution aims at presenting how change po
 int detection of temporal series of estimated feeling and uncertainty can 
 be pursued to identify if and to what extent Italian people modified their
  perception and judgments of price levels from 1994 to 2019. To this goal\
 , we resort to the framework of Atheoretical Regression Trees (ART\, Cappe
 lli et al. 2008) considering the series of monthly response distributions 
 to questions:\n1-(Judgments): How do you think the price level changed ove
 r the previous 12 months? \n2-(Expectations): How do you think the price l
 evel will change over the next 12 months?\nissued by the Italian National 
 Statistical Institute (ISTAT) within the consumers’ confidence survey. R
 esponses are collected over a scale with m=5 categories (1 =`fall '\, 2 = 
 `stay about the same'\, 3 = `rise slightly'\, 4 = `rise moderately'\, 5 = 
 `rise a lot').\nPreliminary results indicate that ART is effective in part
 itioning the series into sub-intervals characterized by different levels o
 f the estimated model parameters\, allowing to study and compare over time
 \, the change points of both feeling and uncertainty. It’s worth noticin
 g that the model parameters refer to two different aspects of the responde
 nts’ perception and judgment of price level\, thus the study of their ch
 ange points may reveal that they show different number and location of bre
 ak dates providing a further and  valuable insight into the two components
  of respondents’  answers.\nPerformances of ART are also discussed compa
 ratively with those of other techniques for structural change point detect
 ion\, in particular with respect to Bai and Perron’s procedure as ART mi
 mics this procedure.\nKeywords: price expectation\; price judgment\; Atheo
 retical Regression Trees\; CUB model\; change point detection\n\nReference
 s:\nC. Cappelli\, R. N. Penny\, W. S. Rea\, M. Reale (2008). Detecting mul
 tiple mean breaks at unknown points in official time series\, MATHEMATICS 
 AND COMPUTERS IN SIMULATION\, Volume 78\, Issues 2–3\, Pages 351-356\, I
 SSN 0378-4754.\nD. Piccolo\, R. Simone (2019). The class of CUB models: st
 atistical foundations\, inferential issues and empirical evidence. STATIST
 ICAL METHOD AND APPLICATIONS\, Volume 28\, pages 389-435.\n\nhttps://indic
 o.unina.it/event/67/contributions/1011/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1011/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mixture Models for Repeatedly Measured Survey Data
DTSTART;VALUE=DATE-TIME:20230629T090000Z
DTEND;VALUE=DATE-TIME:20230629T094000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1019@cern.ch
DESCRIPTION:Speakers: Ralitza Gueorguieva (Yale University)\nSurvey data i
 tems are commonly collected on a Likert scale and may have an additional 
 “don’t know” category. It is also typical to have questions that are
  not applicable to some individuals or to observe floor or ceiling effects
  on ordinal or interval responses. These situations necessitate the use of
  mixture models to properly account for the structure of the data. The mod
 el formulation also needs to account for correlations among repeated measu
 res within individual. We present a couple of mixture models with random e
 ffects for such situations. In particular\, we use logistic sub-models to 
 handle “don’t know”\, inapplicable or floor effects and appropriate 
 generalized linear sub-models for the remaining data. Correlated random ef
 fects link the sub-models together. For illustration we use data from toba
 cco surveys. Maximum likelihood estimation methods are used for model fitt
 ing and inference. The software implementation is using PROC NLMIXED in SA
 S. Simulation studies evaluate bias and efficiency of the parameter estima
 tes.\n\nhttps://indico.unina.it/event/67/contributions/1019/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1019/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Assessing replication success via skeptical mixture priors
DTSTART;VALUE=DATE-TIME:20230629T083000Z
DTEND;VALUE=DATE-TIME:20230629T085000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1014@cern.ch
DESCRIPTION:Speakers: Leonardo Egidi (Università degli Studi di Trieste)\
 nThere is a growing interest in the analysis of replication studies of ori
 ginal findings across many disciplines. When testing a hypothesis for an e
 ffect size\, two Bayesian approaches stand out for their principled use of
  the Bayes factor (BF)\, namely the replication BF (Verhagen and Wakenmale
 rs\, 2014) and the skeptical BF (Pawel and Held\, 2022). In both cases rep
 lication data are used to compare an "advocacy" prior against a benchmark.
  For the replication BF\, the latter is the standard point null hypothesis
  of no effect while for the skeptical BF it represents the prior of somebo
 dy who is unconvinced by the original findings. We propose a novel skeptic
 al mixture prior which incorporates skepticism and limits prior-data confl
 ict. We support our proposal with theoretical results on consistency of th
 e resulting BF\, we illustrate its features on an extended example\, and w
 e apply it to case studies from the Social Sciences Replication Project.\n
 \n**Keywords**: Bayes factor\, consistency\, prior-data conflict\,\nreplic
 ation studies.\n\n**References:**\n\nPawel\, S.\, & Held\, L. (2022). The 
 sceptical Bayes factor for the assessment of replication success. *Journal
  of the Royal Statistical Society Series B: Statistical Methodology*\, 84(
 3)\, 879-911.\n\nVerhagen\, J.\, & Wagenmakers\, E. J. (2014). Bayesian te
 sts to quantify the result of a replication attempt. *Journal of Experimen
 tal Psychology: General*\, 143(4)\, 1457.\n\nhttps://indico.unina.it/event
 /67/contributions/1014/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1014/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bayesian networks for complementing and building gender equality c
 omposite indicators
DTSTART;VALUE=DATE-TIME:20230628T150000Z
DTEND;VALUE=DATE-TIME:20230628T152000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1009@cern.ch
DESCRIPTION:Speakers: Paola Vicard (University Roma Tre)\nComposite indica
 tors are a common choice for synthesizing complex phenomena. Over the year
 s\, they have grown in popularity and are now applied in many social and e
 nvironmental sciences. Among others\, a subject of increasing interest is 
 gender equality analysis. Gender composite indicators\, even if easy to re
 ad\, may provide a limited picture of the problem. Here we discuss the pot
 entiality of Bayesian networks (BNs) to complement and build composite ind
 icators. BNs are powerful tools for explaining the complex association str
 ucture in the dataset and developing scenarios to orient policy-making. He
 re we propose to use BNs to model the association structure among the gend
 er equality index\, its ingredient variables and other context socio-econo
 mic variables. In such a way the synergy between composite indicator and B
 N gives rise to both a monitoring tool for the gender equality gap status 
 and a proactive inferential machine for proposing policies to reduce inequ
 ality. BNs can be also used to build the gender equality index\, and\, in 
 general\, any composite indicator. Specifically\, we focus attention on an
  extension of BNs\, namely Object-Oriented Bayesian networks (OOBNs). The 
 modularity of the OOBN ensures a computational logic that is consistent wi
 th composite indicators\, while also providing additional information abou
 t the relational structure of variables. An example is carried out on Ital
 ian province-level data.\n\n**Keywords:** composite indicator\, gender equ
 ality\, multivariate dependencies\, Object oriented Bayesian networks\n\n*
 *References:**\n\nCowell\, R. G.\, Dawid\, A. P.\, Lauritzen\, S. L.\, and
  Spiegelhalter\, D. J. (1999). Probabilistic Networks and Expert Systems. 
 Springer Verlag\, New York\n\nMusella\, F.\, Vicard\, P. (2015). Object-or
 iented Bayesian networks for complex quality management problems. Quality 
 & Quantity\, 49\, 115–133\n\nhttps://indico.unina.it/event/67/contributi
 ons/1009/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1009/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bayesian MANOVA for the combined evaluation of handwriting evidenc
 e
DTSTART;VALUE=DATE-TIME:20230628T132000Z
DTEND;VALUE=DATE-TIME:20230628T134000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1018@cern.ch
DESCRIPTION:Speakers: Lampis Tzai ()\nForensic science is a broad field th
 at uses scientific principles and technical methods to help with the evalu
 ation of evidence in legal proceedings of criminal\, civil\, or administra
 tive nature. Forensic scientists examine recovered traces that can be give
 n by glass fragments\, fingerprints\, body fluids\, textile fibers\,\ndigi
 tal device data and handwriting. The handwriting examination is a well-kno
 wn field of analysis. Consider a case involving a handwritten document of 
 unknown origin. Handwritten features extracted from this questioned docume
 nt will be compared to those extracted from a document written by a person
  that is suspected of being at the origin of the anonymous document. The p
 ropositions of interest are the following:\n\n$H_p$: the suspect is the au
 thor of the manuscript\;\n\n$H_d$: the suspect is not the author of the ma
 nuscript.\n\nHandwriting individualization is still largely dependent on t
 he experience of the document examiner\, though studies have been conducte
 d with the aim of supporting handwriting examiners to reduce the degree of
  subjectivity of their expertise. Marquis et al. (2005) proposed to increa
 se the degree of objectivity of handwriting analyses by implementing eleme
 nts of Fourier analysis in order to describe the contour shape of loops of
  characters. Specifically\, the characters that contain loops can be descr
 ibed by means of Fourier descriptors\, which can be used to characterize t
 he shape complexity and other geometric attributes. The\nanalyses conducte
 d showed that these features have a good discriminating power.\nWith the a
 im of implementing the use of these handwriting features for handwriting i
 dentification\, Bozza et al. (2008) proposed a Bayesian probabilistic appr
 oach by modeling the data with multivariate Normalinverse-Wishart distribu
 tion (NIW). The value of the evidence is subsequently assessed by means of
  the Bayes factor\, which can be interpreted as a measure of the strength 
 of support provided by the evidence in favor of the hypothesis $H_p$ again
 st the hypothesis $H_d$. This approach was accomplished to take into accou
 nt the correlation between variables\, the variability between-writers and
  within-writer variability. However\, the above model is implemented separ
 ately for each different type of handwritten character. This can be proble
 matic because it can lead to a different conclusion depending on the type 
 of character that is retained.\n\nIn this research\, it is proposed the im
 plementation of a Bayesian Multivariate Analysis of Variance (MANOVA) via 
 using the loop characters as predictors. The indicator of the loop charact
 er is transformed into a dummy variable (corner-point representation)\, so
  that it is possible to model variables describing\nthe handwriting charac
 ters jointly taking into account the variability between characters\, the 
 variability between-writers for every character and within-writer variabil
 ity. The Bayesian MANOVA is compared with the two-level random effect mode
 l (NIW) proposed by Bozza et al. (2008)\, that is implemented by modelling
 \nall characters jointly or separately. Three different methods for estima
 ting the marginal likelihoods are used\; the Generalized Harmonic Mean\, t
 he Laplace-Metropolis and the Bridge Sampling. Finally\, the performances 
 of the NIW and MANOVA models are compared with those of an alternative one
 \, where a conjugate approach is chosen. This does not allow to model the 
 within and between variation separately\, but the marginals can be obtaine
 d analytically.\n\nFirstly\, we estimate the Bayes factor of the two data 
 models for each writer to determine which model is more compatible with th
 e data. Secondly\, there have been selected handwriting features originati
 ng from the same writer or from different writers to evaluate the rate of 
 false negatives (that is cases where the BF is smaller than one for charac
 ters originating from the same source) and false positives (that is cases 
 where the BF is greater than one for characters originating from different
  sources). Finally\, the sensitivity of models is examined in two critical
  aspects: the misleading background information and the choice of degrees 
 of freedom for the Wishart-inverse distribution that is used to model the 
 handwriting variability. With reference to the misleading background infor
 mation\, the prior distributions were elicited by selecting writers charac
 terized by either small or marked differences.\n\n**Keywords** : 1. Handwr
 iting Evidence 2. Fourier Analysis 3. Multivariate Bayesian Modelling 4. B
 ayes Factor 5. Sensitivity\n\n**References**\n\nBozza\, S.\, Taroni\, F.\,
  Marquis\, R. & Schmittbuhl\, M. (2008)\, ‘Probabilistic evaluation of h
 andwriting evidence:\nlikelihood ratio for authorship’\, Journal of the 
 Royal Statistical Society: Series C (Applied Statistics)\n57(3)\, 329–34
 1.\n\nMarquis\, R.\, Schmittbuhl\, M.\, Mazzella\, W. D. & Taroni\, F. (20
 05)\, ‘Quantification of the shape of handwritten\ncharacters: a step to
  objective discrimination between writers based on the study of the capita
 l character o’\,\nForensic Science International 150(1)\, 23–32.\n\nht
 tps://indico.unina.it/event/67/contributions/1018/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1018/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Variable selection via ranking in generalized linear models
DTSTART;VALUE=DATE-TIME:20230628T140000Z
DTEND;VALUE=DATE-TIME:20230628T142000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1007@cern.ch
DESCRIPTION:Speakers: Marcella Niglio (Università degli Studi di Salerno)
 \nIn many empirical domains\, the availability of ultrahigh-dimensional da
 ta has led to the development of feature screening and variable selection 
 procedures aiming to detect the informative variables of datasets and cons
 equently remove unimportant features.\nIn this context\, we propose a rank
 ing-based variable selection procedure that extends the Ranking Based Vari
 able Selection technique (Baranowski et al.\, 2020) to general linear regr
 ession models.\nWe explore the performance of our proposal using both simu
 lated and empirical data. The algorithm is compared to two competitors: i)
  the Extended BIC (Chen and Chen\, 2012)\; ii) the variable selection proc
 edure based on the combination of the Sure Independence Screening (Fan and
  Song\, 2010) and the Elastic Net (Zou and Hastie\, 2005).\n\n**References
 **\n\nBaranowski R\, Chen Y\, Fryzlewicz P (2020)\, Ranking-based variable
  selection for high-dimensional data\, Statistica Sinica\, 30(3)\, 1485-15
 16.\n\nChen J\, Chen Z (2012)\, Extended BIC for small-n-large-P sparse GL
 M. Statistica Sinica\, 22(2)\, 555-574.\n\nFan J\, Song R (2010)\, Sure in
 dependence screening in generalized linear models with NP-dimensionality. 
 The Annals of Statistics\, 38(6)\, 3567-3604.\n\nZou H\; Hastie T (2005)\,
  Regularization and Variable Selection via the Elastic Net. Journal of the
  Royal Statistical Society\, Series B\, 67(2)\, 301-320.\n\nhttps://indico
 .unina.it/event/67/contributions/1007/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1007/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Objective Shrinkage Priors Via Imaginary Data
DTSTART;VALUE=DATE-TIME:20230628T134000Z
DTEND;VALUE=DATE-TIME:20230628T140000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1001@cern.ch
DESCRIPTION:Speakers: Dimitris Fouskakis (National Technical University of
  Athens)\nIn this work\, focus is given in the Bayesian variable selection
  problem for high-dimensional linear regression problems. The use of shrin
 kage priors\, when the number n of available observations is less than the
  number p of explanatory variables\, is a well-established method\, which 
 shares great theoretical and empirical properties. By using imaginary data
  and shrinkage priors as baseline priors\, under the Power-Expected-Poster
 ior (PEP) prior methodology\, objective shrinkage priors are being created
 . In addition\, we explore the idea of augmenting the imaginary design mat
 rix in order to make it with orthogonal columns and thus to produce indepe
 ndent PEP-shrinkage priors\, based on default baseline priors. Under this 
 setup\, properly chosen hyperpriors are placed on the power parameters of 
 the PEP methodology\, in order to produced mixtures of independent priors 
 suitable for the variable selection problem when n << p. This second appro
 ach provides us with algorithmically flexibility and less time-consuming p
 rocedures. We check the theoretical properties of our proposed methods and
  we explore their behavior via simulated studies.\n\nKeywords: Bayesian Va
 riable Selection\; Imaginary Data\; Objective Priors\; Shrinkage Priors\n\
 nhttps://indico.unina.it/event/67/contributions/1001/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1001/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Power Prior's Weight Parameter elicitation via Bayes Factor-Calibr
 ated p-values
DTSTART;VALUE=DATE-TIME:20230629T103000Z
DTEND;VALUE=DATE-TIME:20230629T105000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1017@cern.ch
DESCRIPTION:Speakers: Roberto Macrì Demartino (University of Padova)\nIn 
 recent times\, the integration of historical data in the design and analys
 is of new clinical trials has gained considerable attention\, owing to eth
 ical reasons and difficulties encountered in recruiting patients. In the B
 ayesian framework\, the process of informative prior elicitation is widely
  recognized as a complex and multifaceted undertaking\, requiring the care
 ful quantification and synthesis of prior information into an appropriate 
 prior distribution. Hence\, there is a pressing need for developing techni
 ques and methods that can facilitate synthesizing and quantifying prior in
 formation more effectively and efficiently. Within this context\, the conc
 ept of *power priors* ([Chen and Ibrahim\, 2000][1]) has emerged as a popu
 lar approach for incorporating historical data into the prior distribution
  of a treatment effect\, in a flexible and controlled manner. \nThe power 
 prior methodology heavily relies on the *weight parameter* $\\delta$\, ran
 ging between 0 and 1\, that is a crucial factor in determining the degree 
 to which the historical data influences the prior distribution\, and for w
 hich multiple elicitation strategies are available. A modification of the 
 power prior allows a hierarchical prior specification by taking $\\delta$ 
 as a random quantity\n\n$\\hspace{4.5cm}\n\\pi\\left({\\theta}\, \\delta \
 \mid D_0\\right) \\propto L\\left({\\theta} \\mid D_0\\right)^\\delta \\pi
 _0({\\theta}) \\pi_0(\\delta)\,\n$\n\nwhere $D_0$ is an historical dataset
  with corresponding likelihood $L(\\theta \\mid D_0)$\, $\\pi_0({\\theta})
 $ and $\\pi_0(\\delta)$ are the initial priors for $\\theta$ and $\\delta$
 \, respectively. Furthermore\, a significant benefit of incorporating a no
 rmalizing factor in the power prior methodology is its adherence to the li
 kelihood principle\, as demonstrated by the joint normalized power prior\n
 \n\n$\\hspace{4.5cm}\n    \\pi\\left({\\theta}\, \\delta \\mid D_0\\right)
  = \\frac{L\\left({\\theta} \\mid D_0\\right)^\\delta \\pi_0({\\theta}) \\
 pi_0(\\delta)}{\\int_{\\Theta} L\\left({\\theta} \\mid D_0\\right)^\\delta
  \\pi_0({\\theta}) d {\\theta}}.\n$\n\nConsequently\, in a fully Bayesian 
 approach\, the ability to effectively elicit an appropriate initial prior 
 distribution for the weight parameter $\\delta$ is a crucial step. As far 
 as we know from reviewing the existing literature\, a comprehensive justif
 ication underlying the choice of a Beta distribution with fixed hyper-para
 meters\, that is an usual choice for this framework\, is pretty vague. \n\
 nThe Bayes factor (BF) constitutes a valuable statistical tool for model c
 omparison\; however\, we explore the use of the Bayes Factor to discrimina
 te between competing models that incorporate distinct initial Beta prior d
 istributions for the weight parameter by exploiting some BF $p$-value cali
 bration techniques ([Garcia-Donato and Chen\, 2005][2]). \nThis would enab
 le the selection of candidate models based on a more accurate and reliable
  assessment of the available evidence\, thereby enhancing the validity and
  robustness of statistical inference.\n\n**Keywords:** Beta distribution\,
  Clinical trial\, Historical information\, Robust selection.\n\n\n**Refere
 nces**\n\n 1. Chen\, M.-H. and Ibrahim\, J. G. (2000). Power prior distrib
 utions for regression models. Statistical Science\, 15(1):46 – 60.\n\n 2
 . Garcia-Donato\, G. and Chen\, M.-H. (2005). Calibrating Bayes factor\n  
   under prior predictive distributions. Statistica Sinica\, 15(2):359–38
 0.\n\n  [1]: https://www.jstor.org/stable/2676676\n  [2]: https://www.jsto
 r.org/stable/24307360\n\nhttps://indico.unina.it/event/67/contributions/10
 17/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1017/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Regularized Joint Mixture Models
DTSTART;VALUE=DATE-TIME:20230629T130000Z
DTEND;VALUE=DATE-TIME:20230629T132000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1021@cern.ch
DESCRIPTION:Speakers: Konstantinos Perrakis (Department of Mathematical Sc
 iences\, Durham University)\nRegularized regression models are well studie
 d and\, under appropriate conditions\, offer fast and statistically interp
 retable results. However\, large data in many applications are heterogeneo
 us in the sense of harboring distributional differences between latent gro
 ups. Then\, the assumption that the conditional distribution of response Y
  given features X is the same for all samples may not hold. Furthermore\, 
 in scientific applications\, the covariance structure of the features may 
 contain important signals and its learning is also affected by latent grou
 p structure. We propose a class of mixture models for paired data (X\, Y) 
 that couples together the distribution of X (using sparse graphical models
 ) and the conditional Y | X (using sparse regression models). The regressi
 on and graphical models are specific to the latent groups and model parame
 ters are estimated jointly (hence the name "regularized joint mixtures"). 
 This allows signals in either or both of the feature distribution and regr
 ession model to inform learning of latent structure and provides automatic
  control of confounding by such structure. Estimation is handled via an ex
 pectation-maximization algorithm\, whose convergence is established theore
 tically. We illustrate the key ideas via empirical examples. An R package 
 is available at https://github.com/k-perrakis/regjmix.\n\n*Keywords:* dist
 ribution shifts\, heterogeneous data\, joint learning\, latent groups\, mi
 xture\nmodels\, sparse regression\n\nhttps://indico.unina.it/event/67/cont
 ributions/1021/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1021/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bayesian effect measures for a  location scale model
DTSTART;VALUE=DATE-TIME:20230629T134000Z
DTEND;VALUE=DATE-TIME:20230629T140000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1016@cern.ch
DESCRIPTION:Speakers: Claudia Tarantola (University of Pavia)\nWe consider
  a Bayesian approach for the analysis of rating data when a scaling compon
 ent is taken into account\, thus incorporating a specific form of heterosk
 edasticity.   Our approach includes model-based probability effect measure
 s that enable comparisons of distributions among multiple groups. These ef
 fect measures are adjusted for explanatory variables that have an impact o
 n both the location and scale components. To estimate the parameters of ou
 r fitted model and derive the associated effect measures\, we employ Marko
 v Chain Monte Carlo techniques. Through an analysis of students' evaluatio
 ns of a university curriculum counselor service\, we assess the performanc
 e of our method and highlight its valuable support in the decision-making 
 process. Our findings demonstrate the effectiveness of our approach and em
 phasize its ability to enhance decision-making processes by providing valu
 able insights and support to stakeholders involved.\n\nhttps://indico.unin
 a.it/event/67/contributions/1016/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1016/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bayesian learning of network structures from interventional experi
 mental data
DTSTART;VALUE=DATE-TIME:20230630T075000Z
DTEND;VALUE=DATE-TIME:20230630T081000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1026@cern.ch
DESCRIPTION:Speakers: Stefano Peluso ()\nDirected Acyclic Graphs (DAGs) pr
 ovide an effective framework for learning causal relationships among varia
 bles given multivariate observations. Under pure observational data\, DAGs
  encoding the same conditional independencies cannot be distinguished and 
 are collected into Markov equivalence classes. In many contexts however\, 
 observational measurements are supplemented by interventional data that im
 prove DAG identifiability and enhance causal effect estimation. We propose
  a Bayesian framework for multivariate data partially generated after stoc
 hastic interventions. To this end\, we introduce an effective prior elicit
 ation procedure leading to a closed-form expression for the DAG marginal l
 ikelihood and guaranteeing score equivalence among DAGs that are Markov eq
 uivalent post intervention. Under the Gaussian setting we show\, in terms 
 of posterior ratio consistency\, that the true network will be asymptotica
 lly recovered\, regardless of the specific distribution of the intervened 
 variables and of the relative asymptotic dominance between observational a
 nd interventional measurements. We validate our theoretical results in sim
 ulation and we implement on both synthetic and biological protein expressi
 on data a Markov chain Monte Carlo sampler for posterior inference on the 
 space of DAGs.\n\nhttps://indico.unina.it/event/67/contributions/1026/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1026/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Integrating model-based clustering and graphical models to explore
  the relationship with the digital self-image in (pre)adolescents
DTSTART;VALUE=DATE-TIME:20230630T081000Z
DTEND;VALUE=DATE-TIME:20230630T083000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1015@cern.ch
DESCRIPTION:Speakers: Chiara Brombin (CUSSB\, Faculty of Psychology\, Vita
 -Salute San Raffaele University)\nDigital revolution has dramatically chan
 ged not only the way people interact but also the relationship with the se
 lf-image. Increased data availability and computational power have signifi
 cantly improved algorithms for facial feature detection which have been al
 so successfully applied to develop face filter apps enhancing and “beaut
 ifying” self-portraits.\nPotential of these filters in altering facial a
 ppearance has raised concerns in parents\, educators and health profession
 als as they promote unrealistic beauty standards increasing discrepancy be
 tween real and digital self. Actually taking\, sharing and viewing edited 
 selfies may have detrimental effects especially on younger users in a deve
 lopmental phase where they are already facing significant identity constru
 ction processes\, possibly giving rise to appearance-related cyberbullying
 . \nTo investigate selfie-sharing/editing behaviour in (pre)adolescents\, 
 their relationship with digital self-image\, problematic use of social net
 work and possible internalizing symptoms an online questionnaire\, includi
 ng both validated and ad-hoc realized scales\, has been developed. When ex
 amining the digital-self image\, here the attention is narrowed to the fac
 e only\, the protagonist of real and virtual interactions\, and not to the
  whole body. \nIn this setting\, graphical models represent an appealing t
 ool to model dependence structure between collected variables. To properly
  analyze collected data\, the procedure should account for the fact that (
 i) data from psychological questionnaires are usually measured on discrete
 /ordinal levels thus violating the normality assumption and (ii) measured 
 behaviors are rarely homogeneous and this heterogeneity should be properly
  modelled to obtain unbiased results. \nTo tackle these issues\, an approa
 ch integrating model-based clustering and graphical models (Fop et al.\, 2
 019)\, has been applied to copula transformed data collected on a sample o
 f 229 middle school (pre)adolescents which took part to the online survey.
  A two-clusters solution was selected as best based on BIC criterion: the 
 two clusters actually showed different covariance network and different ma
 nagement of online self-image and psychological status. Participants in th
 e cluster displaying a worse management of online self-image and psycholog
 ical status were mainly female reporting higher use of social networks. To
  better examine the relationships among variables within each cluster\, pa
 rtial correlation networks were estimated separately for the two clusters 
 and  compared using both global and local network statistics and inferenti
 al procedure for network comparison.\nAlthough graphical models have been 
 widely used to model psychological phenomena as complex networks\, the app
 lication to selfie behavior is original. Moreover\, identifying clusters w
 ithin a graphical model framework has important practical implications suc
 h as (i) aiding in the development of tailored training programs suited fo
 r improving digital wellbeing in younger  users and (ii) uncovering new da
 ta-driven relationships among constructs thus generating new hypothesis to
  test in successive studies.\n\nReference\nFop\, M.\, Murphy\, T.B. and Sc
 rucca\, L.\, 2019. Model-based clustering with sparse covariance matrices.
  Statistics and Computing\, 29(4)\, pp.791-819. \nKashihara\, J.\, Takebay
 ashi\, Y.\, Kunisato\, Y. and Ito\, M. (2021). Classifying patients with d
 epressive and anxiety disorders according to symptom network structures: A
  Gaussian graphical mixture model-based clustering. Plos one\, 16(9)\, p.e
 0256902.\n\nhttps://indico.unina.it/event/67/contributions/1015/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1015/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Network Integration with INet algorithm
DTSTART;VALUE=DATE-TIME:20230630T105000Z
DTEND;VALUE=DATE-TIME:20230630T111000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1027@cern.ch
DESCRIPTION:Speakers: Valeria Policastro ()\nNowadays\, network data integ
 ration is a demanding problem and still an open challenge\, especially whe
 n dealing with large datasets. When collecting several data sets and heter
 ogeneous data types on a given phenomenon of interest\, the individual ana
 lysis of each data set will give only a particular view of such phenomenon
 . In contrast\, integrating all the data will widen and deepen the results
  giving a more global view of the entire system.\n\nWe developed a novel s
 tatistical method named INet algorithm\, for data integration based on wei
 ghted multilayer networks. Under the assumption that the structure underne
 ath the different layers has some similarity that we want to emerge in the
  integrated network\, we generate a “consensus network” through an ite
 rative procedure based on structure comparison\, capable of pulling out im
 portant information about the phenomenon under study. The procedure tries 
 to preserve common higher-order structures of the original networks in the
  integrated one\, i.e. neighborhood. Once obtained the consensus network\,
  we compared it with the starting networks extracting “specific networks
 ”\, one for each layer\, containing peculiar information of the single d
 ata type not present in all the others.\n\nWe tested our method on simulat
 ed networks to analyze the performance of our algorithm and we analyzed vi
 rus and vaccine gene co-expression networks to better understand infectiou
 s diseases.\n\nhttps://indico.unina.it/event/67/contributions/1027/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1027/
END:VEVENT
BEGIN:VEVENT
SUMMARY:How to peel the network: an algorithm for weighted triad census
DTSTART;VALUE=DATE-TIME:20230630T103000Z
DTEND;VALUE=DATE-TIME:20230630T105000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1023@cern.ch
DESCRIPTION:Speakers: Roberto Rondinelli ()\nIn Network Analysis\, the int
 eraction between three nodes is called a "triad" and represents the minima
 l group structure that can be observed. According to the presence and the 
 type of the relations between three nodes\, sixteen triadic configurations
  (called the isomorphism classes) are defined and their distribution is de
 noted as "triad census". This kind of analysis is used in different situat
 ions concerning relational data and the conventional approach is well-defi
 ned for unweighted networks. As a consequence\, the information regarding 
 the weights is not taken into account. \nTo exploit this information in th
 e triad analysis\, we propose a new algorithm denoted as "network peeling"
  to count the different configurations of triads in weighted networks. The
  algorithm computes the triad census over the network layers generated at 
 each step. The resulting matrix (with dimensions layers x isomorphism clas
 ses) can be summarized through a set of descriptive measures representing 
 the weighted triad census.\nWith the aim to highlight the appropriateness 
 of our approach\, we consider some real scenarios and a simulation study\,
  comparing weighted and conventional triad censuses.\n\nhttps://indico.uni
 na.it/event/67/contributions/1023/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1023/
END:VEVENT
BEGIN:VEVENT
SUMMARY:φ-Divergence based Modelling of Categorical and Rank Data
DTSTART;VALUE=DATE-TIME:20230629T075000Z
DTEND;VALUE=DATE-TIME:20230629T081000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1022@cern.ch
DESCRIPTION:Speakers: Maria Kateri (RWTH Aachen University)\nStandard mode
 ls for categorical and ordinal data\, such as log-linear\, association mod
 els and logistic regression models for binary or ordinal responses\, as we
 ll as the Mallows model for rank data are revisited and defined through st
 atistical information theoretic properties in terms of the Kullback–Leib
 ler (KL) divergence. In the sequel\, replacing the KL by the φ-divergence
 \, which is a family of divergences including the KL as special case\, the
 se models are generalized to flexible families of models. The suggested mo
 dels are discussed in terms of their properties\, estimation and fit. Fina
 lly\, their potential is illustrated by characteristic examples.\n\nKey-wo
 rds: Cressie–Read power divergence\, distance-based probability models\,
  maximum likelihood estimation\n\nhttps://indico.unina.it/event/67/contrib
 utions/1022/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1022/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Fast Bayesian Variable Screening Using Correlation Thresholds
DTSTART;VALUE=DATE-TIME:20230629T081000Z
DTEND;VALUE=DATE-TIME:20230629T083000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1012@cern.ch
DESCRIPTION:Speakers: Ioannis Ntzoufras (AUEB)\nWe propose a fast Bayesian
  variable selection method for Normal regression models\, using Zellner's 
 $g$-prior specification. The approach is based on using thresholds on Pear
 son and partial correlation coefficients. Nevertheless\, the proposed meth
 odology is derived using purely Bayesian arguments derived from thresholds
  on Bayes factors and posterior model odds. \nThe proposed method can be u
 sed to screen out the non-important covariates and reduce the model space 
 size. Then\, traditional\, computer-intensive\, Bayesian variable selectio
 n methods can be implemented without any problem with the derived reduced 
 model space. \nWe focus on the g-prior where the Bayes factor computations
  and the corresponding correlation thresholds are exact. Nevertheless\, th
 e approach is general and can be easily extended to any prior setup. \nThe
  proposed method is illustrated using simulated examples.\n\nhttps://indic
 o.unina.it/event/67/contributions/1012/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1012/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Modelling ordinal data from repeated surveys
DTSTART;VALUE=DATE-TIME:20230629T073000Z
DTEND;VALUE=DATE-TIME:20230629T075000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1010@cern.ch
DESCRIPTION:Speakers: Marcella Corduas (Departmento of Political Sciences\
 , University of Naples Federico II)\nBusiness and consumers survey data ar
 e the basis for several indicators describing the trend of macro-economic 
 variables that are fundamental for monitoring the overall performance of t
 he economic system. Qualitative surveys typically ask interviewees to expr
 ess their perceptions or expectations about the current or future tendency
  of a reference economic variable (such as inflation or industrial output)
  using a trichotomous  or a finer-tuned ordered scale. Surveys are carried
  out at regular interval by statistical offices\, and collected data are t
 raditionally published in aggregate form\, reporting the proportions of po
 sitive\, neutral or negative assessments. This contribution presents an in
 novative dynamic model that describes the probability distributions of ord
 ered categorical variables observed over time. For this aim\, we extend th
 e definition of the mixture distribution obtained from the Combination of 
 a Uniform and a shifted Binomial distribution (CUB model)\, introducing ti
 me varying parameters. The model parameters identify the main components r
 uling the respondent evaluation process: the degree of attraction towards 
 the object under assessment\, the uncertainty related to the answer\, and 
 the weight of the refuge category that is selected when a respondent is un
 willing to elaborate a thoughtful judgement. We suggest to use the model t
 ime-varying parameters as indicators of the diversity of respondents' opin
 ions\, shifting from an optimistic to a pessimistic state as the surroundi
 ng conditions evolve.  For illustrative purpose\, the dynamic CUB model is
  applied to the consumers' perception and expectations of inflation in Ita
 ly to investigate: a) the effect of the COVID pandemic on the respondents
 ’ perceptions\; b) the impact of the respondents' income level on expect
 ations.\n\n**Keywords:** ordinal data\; CUB model\; consumers' perceptions
 \; consumers' expectations\n\n**References**\n\nCorduas\, M.: A dynamic mo
 del for ordinal time series: An application to consumers'  perceptions of 
 inflation. In:  *Statistical Learning and Modeling in Data Analysis*\, Bal
 zano\, S.\, Porzio\, G.C.\,  Salvatore\, R.\, Vistocco\, D.\,  Vichi\, M. 
 (Eds.)\,   Cham: Springer 2019\, (pp. 37-45).\n\nPiccolo\, D.\, Simone\, R
 . The class of cub models: statistical foundations\, inferential issues an
 d empirical evidence\, (with discussion and rejoinder). *Stat. Meth.\\& Ap
 pl.* 2019\, 28\, 389-435.\n\nhttps://indico.unina.it/event/67/contribution
 s/1010/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1010/
END:VEVENT
BEGIN:VEVENT
SUMMARY:A model-robust subsampling approach in presence of outliers
DTSTART;VALUE=DATE-TIME:20230628T154000Z
DTEND;VALUE=DATE-TIME:20230628T160000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1006@cern.ch
DESCRIPTION:Speakers: Laura Deldossi ()\nIn the era of big data\, several 
 sampling approaches are proposed to reduce costs (and time) and to help in
  informed decision making. Some of these proposals (Drovandi et al.\, 2017
 \; Wang et al.\, 2019\; Deldossi and Tommasi (2022) among others) are insp
 ired to Optimal Experimental Design and require the specification of a mod
 el for the big dataset. \nThis model assumption\, as well as the possible 
 presence of outliers in the big dataset represent a limitation for the mos
 t commonly applied subsampling criterions.\nDeldossi et al. (2023) introdu
 ced non-informative and informative exchange algorithms to select “nearl
 y” D-optimal subsets without outliers in a linear regression model. \n\n
 In this study\, we extend their proposal to account for model uncertainty.
  More precisely\, we propose a model robust approach where a set of candid
 ate models is considered\; the optimal subset is obtained by merging the s
 ubsamples that would be selected by applying the approach of Deldossi et a
 l. (2023) if each model was considered as the true generating process. \nT
 he approach is applied in a simulation study and some comparisons with oth
 er subsampling procedures are provided.\n\nKey-words: Active learning\, D-
 optimality\, Subsampling \n\nReferences\n\nDeldossi\, L.\, Tommasi C. (202
 2) Optimal design subsampling from Big Datasets. Journal of  Quality Techn
 ology 54(1): 93–101\n\nDeldossi\, L.\, Pesce\, E.\, Tommasi\, C. (2023) 
 Accounting for outliers in optimal subsampling methods\, Statistical Paper
 s\, https://doi.org/10.1007/s00362-023-01422-3.\n\nDrovandi CC\, Holmes CC
 \, McGree JM\, Mengersen K\, Richardson S\, Ryan EG (2017) Principles of e
 xperimental design for big data analysis. Statistical  Sciences 32(3): 385
 –404\n\nWang H\, Yang M\, Stufken J (2019) Information-based optimal sub
 data selection for Big Data linear regression. Journal of  American Statis
 tical Association 114(525): 393–405\n\nhttps://indico.unina.it/event/67/
 contributions/1006/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1006/
END:VEVENT
BEGIN:VEVENT
SUMMARY:A structural equation model to integrate item responses\, response
  times and item positioning in students' ability assessment
DTSTART;VALUE=DATE-TIME:20230628T152000Z
DTEND;VALUE=DATE-TIME:20230628T154000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1005@cern.ch
DESCRIPTION:Speakers: Carla Galluccio (Department of Statistics\, Computer
  Science\, Applications "G. Parenti")\nIn the context of students' ability
  assessment\, considering collateral information in addition to item respo
 nses can be helpful in increasing the accuracy of the measurement. In this
  vein\, the evaluation of students' abilities via computer based-devices  
 has made response time data available at the item level (Wang et al.\, 201
 9). Besides\, the literature (Becker et al.\, 2022) has highlighted an ite
 m position effect when the same items are presented in different positions
  within multiple test forms. \n\nWith the present contribution\, we contri
 bute to this research line by proposing a structural equation model (SEM) 
 to jointly consider item responses\, response times and item positioning i
 n students' ability assessment. In particular\, we assume that the respons
 e process is driven by two underlying latent variables: the first latent v
 ariable\, denoted by $\\Theta_i$\, represents the ability of individual $i
 $ that is measured by the test items\; the second latent variable\, denote
 d by $\\eta_i$\, refers to the speediness of individual $i$ to answer the 
 test items.  \n\nWe formulate the statistical model assuming that the item
  responses are directly affected by the ability $\\Theta_i$\, whereas the 
 response times depend both on the ability $\\Theta_i$ and on the speedines
 s $\\eta_i$. Accordingly\, response accuracy tends to increase with the ab
 ility level of individual $i$ while response time tends to decrease with t
 he speediness and ability levels. Moreover\, we suppose that item position
 ing affects both item responses and response time. Under this setting\, th
 e correlation between $\\Theta_i$ and $\\eta_i$ is modelled through the cr
 oss-relation function that models the relationships between $\\Theta_i$ an
 d the observed response times.\n\nThe empirical application of the propose
 d model was carried out on first-year Psychology students at the Universit
 y of Naples Federico II\, attending the introductory Statistics course. Th
 e test administered was composed of 30 multi-choice questions developed ac
 cording to three of the five Dublin descriptors: Knowledge (10 items)\, Ap
 plication (10 items) and Judgement (10 items). For each question\, student
 s' answers were coded as correct (2 credits)\, partially correct (1 credit
 ) and wrong (0 credits). Data were collected through Moodle platform\, whi
 ch also provided the response time.\n\nReferences\n\nBecker\, B.\, Van Rij
 n\, P.\, Molenaar\, D.\, and Debeer\, D. (2022). Item order and speedednes
 s: Implications for test fairness in higher educational high-stakes testin
 g. Assessment & Evaluation in Higher Education\, 47(7):1030–1042.\n\nWan
 g\, C.\, Weiss\, D. J.\, and Su\, S. (2019). Modeling response time and re
 sponses in multidimensional health measurement. Frontiers in psychology\, 
 10:51.\n\nhttps://indico.unina.it/event/67/contributions/1005/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1005/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Subdata selection for big data regression based on leverage scores
DTSTART;VALUE=DATE-TIME:20230628T130000Z
DTEND;VALUE=DATE-TIME:20230628T132000Z
DTSTAMP;VALUE=DATE-TIME:20260909T183301Z
UID:indico-contribution-67-1004@cern.ch
DESCRIPTION:Speakers: Vasilis Chasiotis (Athens University of Economics an
 d Business\, Department of Statistics)\nData continues to become more abun
 dant\, and so the datasets that contain it. Even though big datasets can p
 resent insights and opportunities\, they can pose significant challenges w
 hen it comes to statistical analysis. One of the biggest challenges\, requ
 ired to process and analyze large datasets\, is the computational resource
 s. Regression can be problematic in case of big datasets\, due to the huge
  volumes of data. A standard approach is subsampling that aims at obtainin
 g the most informative portion of the big data. We consider an approach ba
 sed on leverages scores\,  already existing in the current literature for 
 the selection of subdata for linear model discrimination. However\, we hig
 hlight its importance on the selection of data points that are the most in
 formative for estimating unknown parameters. We conclude that the approach
  based on leverage scores improves existing approaches\, providing simulat
 ion experiments as well as a real data application.\n\nhttps://indico.unin
 a.it/event/67/contributions/1004/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1004/
END:VEVENT
END:VCALENDAR