Salvati / Perna / Marchetti Studies in Theoretical and Applied Statistics

SIS 2021, Pisa, Italy, June 21–25
1. Auflage 2023
ISBN: 978-3-031-16609-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 406, 546 Seiten, eBook

Reihe: Springer Proceedings in Mathematics & Statistics

This book includes a wide selection of papers presented at the 50th Scientific Meeting of the Italian Statistical Society (SIS2021), held virtually on 21-25 June 2021. It covers a wide variety of subjects ranging from methodological and theoretical contributions to applied works and case studies, giving an excellent overview of the interests of the Italian statisticians and their international collaborations. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.

Zielgruppe

Research

Autoren/Hrsg.

Salvati, Nicola

Perna, Cira

Marchetti, Stefano

Chambers, Raymond

Inhaltsverzeichnis

A. Cutillo, M. Mazziotta, A. Pareto, A Composite Index of Economic Well-being for the European Union Countries.- Fulvio De Santis and S. Gubbiotti, Fulvio De Santis and S. Gubbiotti, A dynamic power prior for Bayesian non-inferiority trials.- Cinzia Di Nuzzo and S. Ingrassia, A graphical approach for the selection of the number of clusters in the spectral clustering algorithm.- M. Cefis and M. Carpita, A Higher-Order PLS-SEM approach to evaluate football players’ performance.- Simone Del Sarto and P. Coppola and M. Troìa, A latent Markov approach for clustering contracting authorities over time using public procurement red flags.- I. Primerano, F. Santelli and C. Usala, A multiplex network approach for analyzing university students’ mobility flows.- A. Briglia, M. Mucciardi and G. Pirrotta, A statistical model for predicting child language acquisition: unfolding qualitative grammatical development by using logistic regression model.- Fulvio Di Stefano and M. Gasparini, AdaptiveCOVID-19 screening of a subpopulation.- D. Barro, M. Corazza and M. Nardon, Alternative probability weighting functions in behavioral portfolio selection.- C. Scricciolo, Bayesian Quantile Estimation in Deconvolution.- M. Templ, Can the Compositional Nature of Compositional Data be Ignored by using Deep Learning Approaches?.- M. Pratesi, Citizen Data and Citizen Science: a challenge for Official Statistics.- M. Flora and R. Renò, Detecting states of distress in financial markets: the case of the Italian sovereign debt.- Forecasting combination of hierarchical time series: a novel method with an application to CoVid-19.- Raffaele Mattera and Germana Scepi, Frequency domain clustering: an application to time series with time-varying parameters.- Raffaele Grotti, Heterogeneous Income Dynamics.- Carlo Cavicchia, Pasquale Sarnacchiaro and Paolo Montuori, How much do Knowledge about and Attitude toward mobile phone use affect Behavior while driving? An empirical study using a Structural Equation Model.- Isabella Morlini, Impact of emergency online classes on students’ motivation and engagement in University during the Covid-19 pandemic: A study case.- Federico Benassi, Annalisa Busetta*, Gerardo Gallo, Manuela Stranges, Local heterogeneities in population growth and decline. A spatial analysis of Italian municipalities.- Elias Fekhari, Bertrand Iooss, Joseph Muré, Luc Pronzato, and Maria-João Rendas, Model predictivity assessment: incremental test-set selection and accuracy evaluation.- Antonio Naimoli, Giuseppe Storti, Multiple measures Realized GARCH models.- Venera Tomaselli, Giulio Giacomo Cantone, and Vincenzo Miracula, Multiversal Methods in Observational Studies: The Case of COVID-19.- Fiorenzo Ambrosino, Giuseppe Giannini, Antonio Lepore, Biagio Palumbo, Gianluca Sposito.- Neural network for the statistical process control of HVAC systems in passenger rail vehicles.- Salvatore D. Tomarchio, Antonio Punzo and Luca Bagnato, On the use of the matrix-variate tail-inflated normal distribution for parsimonious mixture modeling.- Tiziana Tuoto, Davide Di Cecco and Andrea Tancredi, Population size estimation by repeated identifications of units. A Bayesian semi-parametric mixture model approach.- Ivan Sciascia, Andrea Crosino, Gennaro Carotenuto, Andrea Genre, Spatial interdependence of mycorrhizal nuclear size in confocal microscopy.- Federica Piersimoni and Francesco Pantalone and Roberto Benedetti, Spatially balanced indirect sampling to estimate the coverage of the agricultural census.- Michele Costa, The assessment of environmental and income inequalities.- Righi A., Catanese E., Valentino L., Zardetto D., The Italian Social Mood on Economy Index during the Covid-19 Crisis.- Maria Maddalena Barbieri, Francesca Bassi, Antonio Irpino, Rosanna Verde, The rating of journals and the research outcomes in Statistical Sciences in Italian Universities.- Mauro Bruno, Francesca Inglese, Giuseppina Ruocco, Trusted Smart Surveys: architectural and methodological challenges related to new data sources.- Elena Grimaccia, Alessia Naccarato, Gerardo Gallo, Web surveys: profiles of respondents to the Italian Population Census.

Über Autor(innen)

Nicola Salvati is Associate Professor in Statistics in the Department of Economics and Management, University of Pisa, Italy. He is Associate Editor for the Biometrical Journal, the Journal of the Royal Statistical Society (Series A) and Statistical Methods & Applications. His research is focused on small area estimation, and particularly its use to estimate poverty measures when based on M-quantile and latent variable models. His research interests also include survey sampling, model-assisted and design-based inference, robust regression and spatial statistics. His most recent area of research involves development of new statistical methods based on latent variable models for estimating parameters from non-deterministically linked data.Cira Perna is full professor of Statistics at the Department of Economics and Statistics  of the  University of Salerno (Italy). Her research work mainly focuses on non-linear time series, artificial neural network models and resampling techniques. On these topics, she has published numerous papers in national and international journals. She has participated in several research projects, both at national and international level and she has been a member of several scientific committees of national and international conferences.Stefano Marchetti graduated in Statistical Sciences at the University of Pisa. He got a PhD in Applied Statistics at the University of Florence in 2009. He is Associate Professor in Statistics at the Department of Economics and Management of the University of Pisa. He teaches Statistics in graduate, master and PhD courses of the University of Pisa. His main research interests include Small Area Estimation, M-quantile models, Bootstrap, Poverty estimation and mapping.Ray Chambers is Honorary Professorial Fellow at the National Institute for Applied Statistics Research Australia, University of Wollongong, Australia. He is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association. He was co-Editor in Chief of the International Statistical Review 2015-2019 and has been an Associate Editor for the Journal of Official Statistics, Survey Methodology, the Journal of the Royal Statistical Society (Series A and B) and the Annals of Statistics. He was President of the International Association of Survey Statisticians, 2011-2013 and International Representative on the Board of the American Statistical Association, 2011-2014. His research is focused on robust model-based methods for inference from complex data, particularly where this complexity arises through integration of data from multiple sources. With Chris Skinner, he jointly edited Analysis of Survey Data, Wiley, 2003. More recently, he co-authored Maximum Likelihood Estimation for Sample Surveys, CRC Press, 2012, with David Steel, Alan Welsh and Suojin Wang, and An Introduction to Model-Based Survey Sampling with Applications, Oxford University Press, 2012, with Robert Clark.Ray Chambers is Honorary Professorial Fellow at the National Institute for Applied Statistics Research Australia, University of Wollongong, Australia. He is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association. He was co-Editor in Chief of the International Statistical Review 2015-2019 and has been an Associate Editor for the Journal of Official Statistics, Survey Methodology, the Journal of the Royal Statistical Society (Series A and B) and the Annals of Statistics. He was President of the International Association of Survey Statisticians, 2011-2013 and International Representative on the Board of the American Statistical Association, 2011-2014. His research is focused on robust model-based methods for inference from complex data, particularly where this complexity arises through integration of data from multiple sources. With Chris Skinner, he jointly edited Analysis of Survey Data, Wiley, 2003. More recently, he co-authored Maximum Likelihood Estimation for Sample Surveys, CRC Press, 2012, with David Steel, Alan Welsh and Suojin Wang, and An Introduction to Model-Based Survey Sampling with Applications, Oxford University Press, 2012, with Robert Clark.