CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

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

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

ASA 2021 Statistics and Information Systems for Policy Evaluation

This book includes 25 peer-reviewed short papers submitted to the Scientific Opening Conference titled “Statistics and Information Systems for Policy Evaluation”, aimed at promoting new statistical methods and applications for the evaluation of policies and organized by the Association for Applied Statistics (ASA) and the Department of Statistics, Computer Science, Applications DiSIA “G. Parenti” of the University of Florence, jointly with the partners AICQ (Italian Association for Quality Culture), AICQ-CN (Italian Association for Quality Culture North and Centre of Italy), AISS (Italian Academy for Six Sigma), ASSIRM (Italian Association for Marketing, Social and Opinion Research), Comune di Firenze, the SIS – Italian Statistical Society, Regione Toscana and Valmon – Evaluation & Monitoring

Latent class approaches for modelling multiple ordinal items

The modelling of the latent class structure of multiple Likert items is reviewd. The standard latent class approach is to model the absolute Likert ratings. Commonly, an ordinal latent class model is used where the logits of the profile probabilities for each item have an adjacent category formulation (DeSantis et al., 2008). an alternative developed in this paper is to model the relative orderings, using a mixture model of the relative differences between pairs of Likert items. This produces a paired comparison adjacent category log-linear model (Dittrich et al., 2007; Francis and Dittrich, 2017), with item estimates placed on a (0,1) “worth” scale for each latent class. The two approaches are compared using data on environmental risk from the International Social Survey Programme, and conclusions are presented

ASMOD 2018: Proceedings of the International Conference on Advances in Statistical Modelling of Ordinal Data

[English]:This volume collects the peer-reviewed contributions presented at the 2nd International Conference on “Advances in Statistical Modelling of Ordinal Data” - ASMOD 2018 - held at the Department of Political Sciences of the University of Naples Federico II (24-26 October 2018). The Conference brought together theoretical and applied statisticians to share the latest studies and developments in the field. In addition to the fundamental topic of latent structure analysis and modelling, the contributions in this volume cover a broad range of topics including measuring dissimilarity, clustering, robustness, CUB models, multivariate models, and permutation tests. The Conference featured six distinguished keynote speakers: Alan Agresti (University of Florida, USA), Brian Francis (Lancaster University, UK), Bettina Gruen (Johannes Kepler University Linz, Austria), Maria Kateri (RWTH Aachen, Germany), Elvezio Ronchetti (University of Geneva, Switzerland), Gerhard Tutz (Ludwig-Maximilians University of Munich, Germany). The volume includes 22 contributions from scholars that were accepted as full papers for inclusion in this edited volume after a blind review process of two anonymous referees./ [Italiano]: Il volume raccoglie i contributi presentati alla seconda Conferenza Internazionale “Advances in Statistical Modelling of Ordinal Data” - ASMOD 2018 – che si è svolta presso il Dipartimento di Scienze Politiche, Università di Napoli Federico II, nei giorni 24-26 ottobre 2018. La Conferenza ha visto la presentazione di studi sia teorici che applicati al fine di condividere i più recenti sviluppi scientifici nel campo. Oltre al tema fondamentale dell'analisi delle strutture latenti e dei modelli, i contributi richiamano una vasta gamma di argomenti, tra cui misure di dissimilarità, metodi di clustering, analisi di robustezza, modelli CUB, modelli multivariati e test di permutazione. In particolare, questa pubblicazione contiene le relazioni invitate di studiosi riconosciuti a livello internazionale: Alan Agresti (Università della Florida, USA), Brian Francis (Università Lancaster, Regno Unito), Bettina Gruen (Johannes Kepler University Linz, Austria), Maria Kateri (RWTH Aachen, Germania), Elvezio Ronchetti (Università di Ginevra, Svizzera), Gerhard Tutz (Università Ludwig-Maximilians di Monaco, Germania). Il volume include, inoltre, 22 contributi di studiosi che sono stati accettati dopo un processo di revisione anonima

Deep Learning of the Order Flow for Modelling Price Formation

The objective of this thesis is to apply deep learning to order flow data in novel ways, in order to improve price prediction models, and thus improve on current deep price formation models. A survey of previous work in the deep modelling of price formation revealed the importance of utilising the order flow for the deep learning of price formation had previously been over looked. Previous work in the statistical modelling of the price formation process in contrast has always focused on order flow data. To demonstrate the advantage of utilising order flow data for learning deep price formation models, the thesis first benchmarks order flow trained Recurrent Neural Networks (RNNs), against methods used in previous work for predicting directional mid-price movements. To further improve the price modelling capability of the RNN, a novel deep mixture model extension to the model architecture is then proposed. This extension provides a more realistically uncertain prediction of the mid-price, and also jointly models the direction and size of the mid-price movements. Experiments conducted showed that this novel architecture resulted in an improved model compared to common benchmarks. Lastly, a novel application of Generative Adversarial Networks (GANs) was introduced for generative modelling of the order flow sequences that induce the mid-price movements. Experiments are presented that show the GAN model is able to generate more realistic sequences than a well-known benchmark model. Also, the mid-price time-series resulting from the SeqGAN generated order flow is able to better reproduce the statistical behaviour of the real mid-price time-series

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students