{"id":6,"date":"2015-04-08T11:11:35","date_gmt":"2015-04-08T09:11:35","guid":{"rendered":"http:\/\/people.unipi.it\/cloner\/?page_id=6"},"modified":"2024-08-07T09:59:53","modified_gmt":"2024-08-07T07:59:53","slug":"curriculum","status":"publish","type":"page","link":"https:\/\/people.unipi.it\/nicola_salvati\/curriculum\/","title":{"rendered":"Curriculum"},"content":{"rendered":"

Position: Full Professor in Statistics<\/strong><\/p>\n

Status and place: full time at the University of Pisa<\/strong><\/p>\n

He is Full Professor in Statistics at the University of Pisa, Ph.D. in Applied Statistics.<\/p>\n

Research fields:<\/strong><\/p>\n

His research interests include survey sampling, model-assisted and design-based inference, robust regression, quantile and M-quantile regression, multilevel models, geographically weighted regression, spatial statistics, applications of small area models in poverty mapping, new technologies in survey methodology (computer assisted telephone surveys, electronic data interchange, internet surveys). His work the in the last five years has been published by Journal of the American Statistical Association, Journal of Royal Statistical Society Series A, B and C, Annals of Applied Statistics, International Statistical Review, Wiley Interdisciplinary Reviews: Computational Statistics, Statistics in Medicine, TEST, Statistical Methods in Medical Research.<\/p>\n

Professional experience:<\/strong><\/p>\n

He has participated and he is participating in research programmes at national and international level:<\/p>\n

2023-2025 Progetto PRIN<\/strong>, Quantification in the Context of Dataset Shift (QuaDaSh), Re- sponsabile scientifico: Consiglio Nazionale delle Ricerche. (Co-principal Investigator<\/strong>)<\/p>\n

2023-2024 MAPPE <\/strong>Metodi di stimA per Piccole aree per la Poverta\u0300 Educativa. Programma PE GRINS – GRINS – GROWING RESILIENT, INCLUSIVE AND SUSTAINABLE\u201d (cod. PE0000018). (Principal Investigator<\/strong>)<\/p>\n

2022-2024 Tender EMOS<\/strong>: Provision of services to implement European Master in Official Statistics (EMOS), activities 2022-2023, G.A. No. 2021.0261 (contratto finanziato dalla Commissione Europea). (Investigator<\/strong>)<\/p>\n

2022-2024 Istat – Istituto Nazionale di Statistica, Gruppo di lavoro inter-dipartimentale<\/strong> – Task force Commissione scientifica sulla poverta\u0300 educativa, Esperto esterno all\u2019Istituto, parte del gruppo di lavoro, i cui componenti sono stati individuati sulla base delle loro competenze sui temi del gruppo di lavoro e delle attivita\u0300 di ricerca pregresse condivise e condotte insieme con l\u2019Istat. (Ricercatore Esperto Esterno<\/strong>)<\/p>\n

2022-2024 Istat – Istituto Nazionale di Statistica, Gruppo di lavoro inter-dipartimentale<\/strong> – Task force Metodologie di stima per piccole aree per la produzione di statistiche sul reddito, sulle spese delle famiglie, sugli indicatori di poverta\u0300, sull\u2019utilizzo di tecnologie di informazione e comunicazione, e sugli indicatori sulla salute e sulla sicurezza delle persone per domini di stima non pianificati, Esperto esterno all\u2019Istituto, parte del gruppo di lavoro, i cui componenti sono stati individuati sulla base delle loro competenze sui temi del gruppo di lavoro e delle attivita\u0300 di ricerca pregresse condivise e condotte insieme con l\u2019Istat. (Ricercatore Esperto Esterno<\/strong>)<\/p>\n

2019-2023 LOCOMOTION<\/strong> Low-carbon society: an enhanced modelling tool for the transition to sustainability, G.A. N. 821105 (Horizon 2020). Scientific coordinator Universidad de Valladolid, Spain. (Investigator<\/strong>)<\/p>\n

2017-2021 InGRID-2<\/strong> Integrating Research Infrastructure for European expertise on Inclusive Growth from data to policy, G.A. N. 730998 (Horizon 2020 research and innovation programme). Scientific coordinator Monique Ramioul e Guy Van Gyes, HIVA, Belgio. (Investigator<\/strong>)<\/p>\n

2018-2019 Progetto di Ricerca di Ateneo PRA_2018_9<\/strong> From survey-based to register-based statistics: a paradigm shift using latent variable models. Scientific coordinator Prof. Nicola Salvati. (Principal<\/strong> Investigator<\/strong>)<\/p>\n

2013-2017 Inclusive Growth Research Infrastructure Diffusion, G.A. no. 312691 (SEVENTH FRAMEWORK PROGRAMME – Capacities). Scientific coordinator Monique Ramioul e Guy Van Gyes, HIVA, Belgio. (Investigator<\/strong>)<\/p>\n

2014-2017 PRIN Project ‘Ricchezza delle famiglie e disoccupazione giovanile: metodologie innovative d’indagine statistica per le sfide attuali (Unit\u00e0 di Ricerca Locale di Pisa, local Scientific coordinator Dott. N. Salvati, Scientific coordinator prof. M.G. Ranalli, Universit\u00e0 di Perugia). (Co-Principal<\/strong> Investigator<\/strong>)<\/p>\n

2012-2014 e-Frame European Framework for Measuring Progress, G.A. no. 290520 (SEVENTH FRAMEWORK PROGRAMME – DG Research and Innovation, Theme 8, Socio-economic Sciences and Humanities). Scientific coordinator Marina Signore, Istat. (Investigator<\/strong>)<\/p>\n

2008-2011 Small Area Methods for Poverty and Living Condition Estimates G.A. no. 217565 (SEVENTH FRAMEWORK PROGRAMME – THEME FP7-SSH-2007-1 Socio- economic sciences and the Humanities Part 8). Scientific coordinator prof. M. Pratesi, Dipartimento di Statistica e Matematica Applicata all\u2019Economia, Universit\u00e0 di Pisa. (Investigator<\/strong>)<\/p>\n

Teaching experience<\/strong><\/p>\n

He taught courses in Statistics, Survey Sampling and Social Statistics at the University of Florence and Pisa.<\/p>\n

Selected publications in the last ten years<\/strong><\/p>\n

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Merlo, L., Petrella, L., Salvati, N.<\/strong> & Tzavidis, N. (2023). Unified unconditional regression for multivariate quantiles, M-quantiles and expectiles. Journal of the American Statistical Association T&M<\/em>, https:\/\/doi.org\/10.1080\/01621459.2023.2250512.<\/p>\n

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Lahiri, P. & Salvati, N.<\/strong> (2023). A Nested Error Regression Model with High Dimensional Parameter for Small Area Estimation. Journal of the Royal Statistical Society. Series B: Statistical Methodology,<\/em> 85, 212-239, https:\/\/doi.org\/10.1093\/jrsssb\/qkac010.<\/p>\n

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Chambers, R., Fabrizi, E., Ranalli, M.G., Salvati, N.<\/strong> & Wang, S. (2023). Robust regression using probabilistically linked data. Wiley Interdisciplinary Reviews: Computational Statistics<\/em>, 15, e1596, https:\/\/doi.org\/10.1002\/wics.1596.<\/p>\n

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Salvati, N.<\/strong>, Fabrizi, E., Ranalli, M.G. & Chambers, R. (2021). Small Area Estimation with Linked Data. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 83, 78-107.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

Schirripa-Spagnolo, F., Salvati, N.<\/strong> & D’Agostino, A. (2020). The use of sampling weights in M-quantile random-effects regression: an application to Programme for International Student Assessment mathematics scores, Journal of Royal Statistical Society Series<\/em> C,<\/em> 69, 991-1012.
\n<\/span><\/p>\n

Frumento, P. & Salvati, N.<\/strong> (2020). Parametric modelling of M-quantile regression coefficient functions with application to small area estimation. Journal of Royal Statistical Society Series<\/em> A,<\/em> 183, 229-250.<\/p>\n

Del Sarto, S., Marino, M.F., Ranalli, M.G., Salvati, N.<\/strong> (2019). Using finite mixtures of M-quantile regression models to handle unobserved heterogeneity in assessing the effect of meteorology and traffic on air quality. Forthcoming in Stochastic Environmental Research and Risk Assessment<\/em>, 33<\/strong>, 1345-1359.<\/p>\n

Marino, M.F., Ranalli, M.G., Salvati N.<\/strong> & Alf\u00f2, M. (2019) Semiparametric Empirical Best Prediction for Small Area Estimation of Unemployment\u00a0 Indicators. Annals of Appied Statistics<\/em>, 13, 2, 1166-1197, https:\/\/doi.org\/10.1214\/18-AOAS1226.<\/p>\n

Marchetti, S., Berkesewicz, M., Salvati, N.<\/strong>, Szymkowiak M. & Wawrowski L (2018). The use of a three-level M <\/em>-quantile model to map poverty at local administrative unit 1 in Poland. Journal of Royal Statistical Society Series<\/em> A, <\/em>181<\/strong>, 1077-1104.<\/em><\/p>\n

Bianchi, A., Fabrizi, E., Salvati, N.<\/strong> & Tzavidis, N. (2018). Estimation and testing in M-quantile Regression with applications to small area estimation. International Statistical Review<\/em>, 86<\/strong>, 541-570.<\/p>\n

Alf\u00f2 M., Salvati N.<\/strong> & Ranalli M.G. (2017). Finite mixtures of quantile and M-quantile regression models. Statistics & Computing<\/em>, 27<\/strong>, 547-570.<\/p>\n

Chandra H., Salvati N.<\/strong> & Chambers R. (2017). Small area prediction of counts under a non-stationary spatial model. Spatial Statistics,<\/em> 20, 30-56.<\/p>\n

Tzavidis N., Salvati N.<\/strong>, Schmid T., Flouri E. & Midouhas E. (2016). Longitudinal analysis of the Strengths and Difficulties Questionnaire scores of the Millennium Cohort Study children in England using M-quantile random effects regression. Journal of Royal Statistical Society Series A. 179<\/strong>, 427-452.<\/p>\n

Chambers R., Salvati N.<\/strong> & Tzavidis N. (2016). Semiparametric small area estimation for binary outcomes with application to unemployment estimation for Local Authorities in the UK. Forthcoming in Journal of Royal Statistical Society Series <\/em>A, 179<\/strong>, 453-479.<\/p>\n

Borgoni R., Del Bianco P., Salvati N.<\/strong>, Shmid T. & Tzavidis N. (2016). Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression. Statistical Methods in Medical Research<\/em>. doi:10.1177\/0962280216636651.<\/p>\n

Tzavidis, N., Ranalli, M.G., Salvati, N.<\/strong>, Dreassi, E. & Chambers, R. (2015). Robust small area prediction for counts. Statistical Methods in Medical Research<\/em>, 24<\/strong>, 373-395.<\/p>\n

Chambers, R., Dreassi, E. & Salvati, N.<\/strong> (2014). Disease Mapping via Negative Binomial Regression M-quantiles. Statistics in Medicine<\/em>, 33<\/strong>, 4805-4824.<\/p>\n

Chambers R., Chandra H., Salvati N.<\/strong> & Tzavidis N. (2014). Outlier Robust Small Area Estimation. Journal of Royal Statistical Society Series <\/em>B, 76<\/strong>, 47-69.<\/p>\n

Chambers R., Dreassi E. & Salvati N.<\/strong> (2014). Disease Mapping via Negative Binomial Regression M-quantiles. Statistics in Medicine<\/em>, 33<\/strong>, 4805-4824.<\/p>\n

Dreassi E., Ranalli M.G. & Salvati N.<\/strong> (2014). Semiparametric M-quantile Regression for Count Data. Statistical Methods in Medical Research<\/em>, 23, 591-610.<\/p>\n","protected":false},"excerpt":{"rendered":"

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