ASMOD 2018

Francesca Di Iorio; Rosaria Simone; Stefania Capecchi

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

Authors

Francesca Di Iorio

University of Naples Federico II

https://orcid.org/0000-0002-9586-3380

Rosaria Simone

University of Naples Federico II

https://orcid.org/0000-0002-6844-6418

Stefania Capecchi

University of Naples Federico II

https://orcid.org/0000-0003-2842-8132

Keywords:

Ordinal Data, Statistical Models, Mixture Models

Synopsis

Publisher: FedOA - Federico II University Press

Series: School of Human and Social Sciences. Working Papers.

Pages: X, 219.

Language: English.

NBN: http://nbn.depositolegale.it/urn:nbn:it:unina-23866

Abstract: 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.

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Author Biographies

Francesca Di Iorio, University of Naples Federico II

Francesca Di Iorio is associate professor in Economic Statistics at the Department of Political Sciences, University of Naples Federico II where she has been previously a researcher since 2001. She holds a Ph.D. in Applied Statistics and has previously (1996–2001) worked at the Italian National Statistical Institute (ISTAT). She teaches Economic Statistics and Econometrics. She has published several articles in these fields including non-stationary panels, bootstrap inference, and computational solution for indirect inference, time series analisys and Granger-causality, regressions trees.

Rosaria Simone, University of Naples Federico II

Rosaria Simone is a researcher in Statistics at University of Naples Federico II, Department of Political Sciences. Her current research interests in statistical methodology include models for categorical and ordinal data, demographic and stochastic mortality studies. Other research activities concern computational aspects of statistics and R programming. In March 2015, she awarded an international Ph.D. in Mathematics at University of Basilicata defending a dissertation on limit theorems in mathematical statistics and combinatorial analysis. She teaches Statistical Modelling to MA students at UNINA and Statistics to Bachelor students at the University of Basilicata and UNINA. She has been visiting researcher at Université du Luxembourg (Luxembourg), University of Ghent (Belgium) and University of Munich (Germany).

Stefania Capecchi, University of Naples Federico II

Stefania Capecchi is adjunct professor of Statistics for Psychology at the School of Psychology of the University of Naples Federico II (UNINA). Ph.D. in Social Sciences, she works as lab supervisor at UNINA. Currently, she supports the Statistics research team of the Department of Political Sciences, UNINA and is responsible for the Laboratory for Statistical Data Analysis. She has been a visiting researcher at Eurofound (Dublin, Ireland) and at OECD (Paris, France) and is a member of several national and international research projects on ordinal data, mainly in the fields of policy evaluation, educational outcomes measurement, consumers’ preferences, and well-being assessment.