Conference Program

Francesco Maria Chelli, President of the Italian Statistical Institute - ISTAT
Matteo Cervellati, Coordinator of the GRINS project – PNRR
Marcello Chiodi, President of the Italian Statistical Society – SIS
Daniela Cocchi, University of Bologna
Silvia De Nicolò, University of Bologna
Maria Rosaria Ferrante, University of Bologna
Natalie Shlomo, University of Manchester
Carlo Trivisano, University of Bologna

> Adjusting survey estimates with multi-accuracy post-processing
Fischer-Abaigar, U., LMU, MCML; Kern, C., LMU, MCML; Kreuter, F., LMU, MCML, UMD

> Generalized tree-based machine learning methods with applications to small area estimation
Frink, N., Freie University of Berlin ; Schmid, T., Otto-Friedrich University of Bamberg

> Statistical inference for a finite population mean with machine learning-based imputation for missing survey data
Haziza, D., University of Ottawa; Dagdoug, M., Mc Gill University

> Multiple frame estimation with administrative and volunteer panel data
Bianchi, A., University of Bergamo; Shlomo, N., University of Manchester

> Variance estimation and robustness in integrated estimation from probability and non-probability samples
Čiginas, A., Vilnius University; Krapavickaitė, D., Lithuanian Statistical Society; Nekrašaitė-Liegė, V., Vilnius Gediminas Technical University

> Some recent research at Statistics Netherlands on combining probability and non-probability samples
Scholtus, S., Statistics Netherlands

> Population size estimation and consensus ethnicity classification for domains using multiple sources
Smith, P.A., University of Southampton; van der Heijden, P.G.M., Utrecht University and University of Southampton; Cruyff, M., Utrecht University; Pantalone, F., University of Southampton; Diener, H., Statistics New Zealand; Dunstan, K., Statistics New Zealand

> Exploring M2M transmission for short-term business official surveys in the industry 5.0 era
Distefano, D., ISTAT; Papa, P., ISTAT; Bosso, P., ISTAT; Di Paolo, G.G., ISTAT

> Exploring patterns and determinants of agricultural carbon footprint in Northern Italy: Small area models integrating survey, census and remote sensing data
Borgoni, R., University of Milano-Bicocca; Carillo, F., CREA; Maranzano, P., University of Milano-Bicocca; Pajno, R., University of Milano-Bicocca

> Mismatched measures: Comparing asset-based wealth index and income/consumption poverty in Mozambique
Allorant, A, University of Southampton; Perfetti Villa, L, University of Southampton; Luna Hernandez, A, University of Southampton; Tzavidis, N, University of Southampton

> Are we reaching the poor? Evaluating Austria’s social assistance schemes via small area estimation
Broka, A, University of Bologna; De Nicolò, S, University of Bologna; Angel, S, Austrian Institute of Economic Research

> Area-level small area estimation with random forests
Harmening, S., Otto-Friedrich University of Bamberg; Lee, Y., Otto-Friedrich University of Bamberg; Runge, M., Freie University of Berlin; Schmid, T., Otto-Friedrich University of Bamberg

> Improving prediction accuracy in small area models via clusterwise regression
Maranzano, P., University of Milano-Bicocca; Mattera, R., Luigi Vanvitelli University of Campania; Sugasawa, S., Keio University

> On some new diagnostic measures for area-level models in small area estimation
Marcis, L., University of Cassino and Southern Lazio; Pagliarella, M.C., University of Cassino and Southern Lazio; Salvatore, R., University of Cassino and Southern Lazio

> A comparative study of bootstrap confidence intervals in small area estimation
Mori, L., University of Bologna; Ferrante, M. R., University of Bologna; Tizianel, E., University of Bologna

> Estimating the variance of basic summary statistics in the absence of complete design information. A parametric simulation study
Stacchini, A., University of Bologna

> On the use of bootstrap to enhance variance estimation for more than one-frame surveys
Ievoli, R., University of Ferrara; Cocchi, D., University of Bologna

> Evaluation of anonymised georeferenced data
Gril, L., Freie University of Berlin; Rendtel, U., Freie University of Berlin

> Survey sampling and differential privacy
Bonnéry, D. Insee, Jamme J., Insee

> Using perturbative methods for magnitude tables in statistical disclosure control
Sabolová, R., Statistics Sweden; Adriansson, N., Statistics Sweden; Tepe, Ö., Statistics Sweden

> Imputing local income distributions with SSIT-GAMLSS
Betti, G. University of Siena; Mori, L., University of Bologna

> Parametric and semi-parametric methods for estimating poverty transition with synthetic panels
D'Alberto, R., University of Verona; De Nicolò, S., University of Bologna; Gardini, A., University of Bologna

> Editing and imputation: An official statistics perspective
Rosati, S., ISTAT; Filippini, R., ISTAT; Toti, S., ISTAT

> Roots, trends and emerging horizons of multiple frame surveys
Mecatti, F., University of Milano-Bicocca

> Use of multiple frames in the integration of probability and non-probability surveys
Cobo, B., University of Granada; Rueda, M.M., University of Granada; Rueda, J.L., University of Granada

> Multiple frame surveys in modern data integration
Saegusa, T., University of Maryland

> Sanitary migration in Italy: A model-based approach
Arima S., University of Salento; Angelelli, M., University of Salento, Polettini, S., Sapienza Università di Roma, Ciavolino, E., University of Salento

> Poverty prevalence among people with disability: A small area estimation approach
Fabrizi, E., Università Cattolica del S. Cuore

> A spatially geographically weighted Fay-Herriot model for small area estimation – An application to poverty indicators in Italy
Schirripa Spagnolo, F., University of Pisa; Giusti, C., University of Pisa; Moretti, A., Utrecht University; Salvati, N., University of Pisa

> Unit-level models for multivariate binary data: Small area estimation of social inclusion indicators
Failli, D.,University of Perugia; Marino, M. F., University of Firenze; Ranalli, M. G., University of Perugia

> Indirect sampling for the spatialization of maritime fishing activities: The case of Valpena.
Medous, E., IGN; El Ghaziri, A., Institut Agro Anger-Rennes; Rollo, N., LETG Nantes; Trouillet, B., LETG Nantes; Bellanger, L., Nantes University; Dieudonée, E., LETG Nantes

> Variance estimation for spatially balanced sampling designs: A review and new results
Pantalone, F., University of Southampton; Benedetti, R., “G. d’Annunzio” University; Piersimoni, F., ISTAT; Ranalli, M.G., University of Perugia

> Sampling artificial stands: Can spatial information play a role?
Dickson, M.M., University of Padova; Bucci, G., CNR-Institute of Biosciences and BioResources; Ioveno, P., CNR-Institute of Biosciences and BioResources; Marchi, M., CNR-Institute of Biosciences and BioResources; Puletti, N., CREA; Serio, R.G., University

> Predictor selection under misspecified models
Żądło, T., University of Economics in Katowice; Chwila, A., University of Economics in Katowice; Hadaś-Dyduch, M., University of Economics in Katowice; Krzciuk, M., University of Economics in Katowice; Stachurski, T., University of Economics in Katowice; Wolny-Dominiak, A., University of Economics in Katowice

> Modelling workplaces and commuting flows using survey and administrative data
Pavasare, R., Central Statistical Bureau of Latvia

> Adapting a small area estimation model for non-probability sample data integration
Burakauskaitė, I., Vilnius University and State Data Agency (Statistics Lithuania); Čiginas, A., Vilnius University and State Data Agency (Statistics Lithuania); Šlevinskas, D., State Data Agency (Statistics Lithuania)

> When non-response makes estimates from a census a small area estimation problem: The case of the survey on graduates’ employment status in Italy
Ranalli, M.G., University of Perugia; Pennoni, F., University of Milan Bicocca; Bartolucci, F., University of Perugia; Mira, A., University of Svizzera Italiana e University of Insubria

> Integrating datasets from finite populations: A focus on registries and self-selected surveys
Nicolussi, F., Politecnico di Milano; Masci, C., University of Milan; Bertarelli, C., University Ca’ Foscari of Venezia; Terzera, L., University of Milan-Bicocca; Mecatti, F., University of Milan-Bicocca

> Small area estimation using area level linked data
Chambers, R., Australian National University; Salvati, N., University of Pisa; Fabrizi, E., Università Cattolica del Sacro Cuore

> Conformalized Bayesian prediction in official statistics
Liseo, B., Sapienza University of Rome; Deliu, N. Sapienza University of Rome

> Assessing uncertainty when integrating Mobile Network Operator data into official statistics with some transfer learning methods
Tuoto, T., ISTAT; Zhang, L.-C., Southampton University and Statistics Norway

> Accuracy measures for text mining methods applied to public administration PIAOs
Righi, P., ISTAT; Bianchi, G, ISTAT; Giubilei, R., Food and Agriculture Organization of the United Nations; Naccarato, A., Roma Tre University

> Small area estimation under several spatial specifications
Billé, A.G., University of Bologna; Ferrante, M.R., University of Bologna; Salvati, N., University of Pisa

> Measuring educational poverty in Italy: A MIRT-SAE approach
Del Sarto, S., University of Perugia; Ranalli, M.G., University of Perugia; Bertarelli, G., University of Venice; Guandalini, A., ISTAT; Pratesi, M., University of Pisa

> Municipal-level estimation of tourism perception: A machine learning-based approach to small area estimation
Niederhametner, N., Statistics Austria; Daul, R., Statistics Austria

> Correcting selection bias in non-probability two-phase payment survey
Chen, H., Bank of Canada; Tsang, J., University of Ottawa

> Data-driven sampling: A new framework for correcting political bias in probability-based online panels
Neiger, D., Social Research Centre; Barton, J., Social Research Centre; Phillips, B., Social Research Centre; Ward, A. C., Social Research Centre

> The use of meta-information in adjusting non-ignorable selection bias
Arletti, A., University of Padua; Si, Y., University of Michigan; Letizia, M. L., University of Padua; Paccagnella, O., University of Padua

> Sampling strategies for spatial phenomena leveraging auxiliary information
Bocci, C., University of Florence; Rocco, E., University of Florence

> An automated approach for tuning precision constraints in an optimal allocation problem
Barcaroli, G.; Bombelli, I., ISTAT; Fasulo, A., EUROSTAT; Guandalini, A., ISTAT; Sacco, G., ISTAT; Terribili, M. D., ISTAT.

> Review of procedures for informative sampling
Eideh, A.A., Al-Quds University

> An interactive tool for statistical matching
Guastadisegni, L., University of Bologna; Trivisano, C., University of Bologna

> Integrating social survey data: A comparative evaluation of statistical matching techniques
Fontanella, L., University of Chieti-Pescara; Cucco, A., University of Chieti-Pescara; Aretusi, G., University of Chieti-Pescara; del Gobbo, E., University of Foggia; Sarra, A., University of Chieti-Pescara

> On the sampling distribution of the ordinary least square estimator in parametric statistical matching
Narcisi, M., University of Bologna

> Bayesian integration of different data sources: Balancing cost, bias and measurement
Salvatore, C., Utrecht University; Sakshaug, J., LMU-Munich and IAB; Wisniowski, A., Manchester University; Struminskaya, B., Utrecht University; Biffignandi, S., University of Bergamo

> Make valid and reliable inferences about technological innovation and Industry 4.0 of firms in Tuscany using data from web scraping
Braito, L., University of Florence; Rocco, E., University of Florence; Lodetti, P., Baloon srl

> Improving the sampling strategy for the Community Innovation Survey using machine learning algorithms
Klingwort, J., Statistics Netherlands; van Berkel, K., Statistics Netherlands; van den Brakel, J., Statistics Netherlands

> Statistical frameworks for data integration and beyond
Wu, C. University of Waterloo

> Beyond string matching: Embeddings and artificial intelligence for statistical integration
Bruno, M., ISTAT

> Integrating big data and alternative sources into small area estimation
Pratesi, M., University of Pisa

> Confidentiality and differential privacy in the dissemination of frequency tables
Shlomo, N., University of Manchester, Rinott, Y., Hebrew University, O'Keefe, C., CSIRO Australia, Skinner, C., The London School of Economics and Political Science

> On the formal privacy guarantees of synthetic data (generated without formal privacy guarantees)
Neunhoeffer, M., Institute for Employment Research & Ludwig-Maximilians University of München; Seeman, J., Urban Institute & University of Michigan; Drechsler, J., Institute for Employment Research & Ludwig-Maximilians University of München

> PrivGen: A human-in-the-loop approach to generate private synthetic data
Nobani, N., University of Milan-Bicocca; Officioso, G., University of Milan-Bicocca; Mezzanzanica, M,. University of Milan-Bicocca; Sperlì, G., University of Naples Federico II; Mercorio, F,.University of Milan-Bicocca

> Stratifying the second stage sample units via marginal allocation: An innovative two-stage sampling design for Istat social surveys
Asti, L., ISTAT; De Vitiis, ISTAT; C., Inglese, F. ISTAT; Terribili, M., ISTAT; Trambusti, D. ISTAT;

> Evidence supporting the design of the Italian Integrated System of Social Surveys
Guandalini, A., ISTAT; Loriga, S., ISTAT; Terribili, M.D., ISTAT

> Some applications of optimality principles to sample selection from a register
Tillé, Yves, University of Neuchatel

> Defining ad-hoc sampling designs for small area estimation
Nardelli, V., Università Cattolica del Sacro Cuore; Falorsi, P. D., Sapienza University of Roma, Italy; Falorsi, S., ISTAT; Righi, P., ISTAT

> Weighting survey data for respondent embeddedness in a recruitment environment: Rationale, experiences and challenges
Albert, E., University of Vienna

> Strategies for dealing with digital inequality in self-completion survey designs – Evidence from Germany
Cornesse, C.,GESIS

> Calibration with bagging of the principal components on a large number of auxiliary variables
Hasler, C., University of Neuchâtel and University of Zürich; Tripet, A., University of Neuchâtel; Tillé, Y., University of Neuchâtel

> Two-step calibration of design weights under dual frame surveys
Kumar, S., Banaras Hindu University; Rai, P. K., Banaras Hindu University

> A neutrosophic general class of calibration estimators for population median using stratified sampling in presence of non-sampling errors
Pandey, M. K., Indian Institute of Technology (Indian School of Mines); Singh, G. N., Indian Institute of Technology (Indian School of Mines)

> Accurate inference for SAE models
Victoria Feser, M.-P., University of Bologna; Orso, S., University of Geneva; Guerrier, S., University of Geneva; Karemera, M., University of Geneva

> A dynamic adaptive sampling approach for spatial data
Altieri L., University of Bologna; Cocchi D., University of Bologna

> Spatially balanced sampling for italian social surveys
Falorsi, S., Istat; Benedetti, R., University "G. d'Annunzio" Chieti – Pescara; Loriga, S., ISTAT; Piersimoni, F., ISTAT; Russo, M., ISTAT

> Spatial gradient-oriented transects for sampling rare populations
Carrer, F., Swedish University of Agricultural Sciences; Prentius, W., Swedish University of Agricultural Sciences; Grafström, A., Swedish University of Agricultural Sciences

> A non-probabilistic sample survey to study the regional variability in attitudes towards homosexuality: Survey design and weighting aspects
Moretti, A., Utrecht University; Salvatore, C., Utrecht University; Meitinger, K., Utrecht University

> Alternative selection mechanisms in online surveys
Rendtel, U., Freie University of Berlin; Prücklmair, F., Otto-Friedrich University of Bamberg

> Harnessing social media for innovative data collection: A Reddit scraping and data-cleaning pipeline
Stracqualursi, L., University of Bologna

> Assigning unit weights for linked data
Liu, A.-C., Utrecht University; Lugtig, P., Utrecht University; Scholtus, S., Statistics Netherlands; De Waal, T., Statistics Netherlands, Tilburg University

> Machine learning approaches for the estimation of response probabilities in the weighting procedure of EU-SILC in Austria
Glaser, T., Statistics Austria

> Using Administrative Data and Machine Learning for Nonresponse Weighting in Official Statistics: Evidence from the Icelandic Labour Force Survey
Sakshaug, J.W., IAB & LMU-Munich; Einarsson, H., University of Iceland

> Statistical inference when data come from different sources: Perspectives, limitations, and pitfalls
Conti, P. L., Sapienza University of Rome

> Data integration for mapping vegetation biodiversity
Golini, N., University of Turin; Zoppi G., University of Turin; Lanteri, A., University of Turin; Cameletti, M., University of Bergamo; Ignaccolo, R., University of Turin; Lo Presti, A., University of Turin

> On admissibility in bipartite incidence graph sampling
Zhang, L.C., University of Southampton & Statistisk sentralbyrå

> The role of participant understanding in data donation studies
Boeschoten, L., Utrecht University; McCool, D., Utrecht University, Struminskaya, B., Utrecht University

> Collecting expenditure data with a household budget app in Germany. Participation behavior, non-participation bias, and receipt scanning
Keusch, F., University of Mannheim; Fritz, M., University of Mannheim; Volk, J., Destatis; Häufglöckner, L., Destatis

> Innovative approach to real-time data collection: Insights from an in-the-moment survey of beachgoers in Spain
Rivilla, K. S., RECSM University Pompeu Fabra; Ochoa, C., RECSM University Pompeu Fabra; Revilla, M., RECSM University Pompeu Fabra

> Gradient boosting for hierarchical data in small area estimation
Messer, P., University of Bamberg; Schmid, T., University of Bamberg

> A support-vector mixed effects regression model for small area estimation
Berg, E., Iowa State University; Pham, H., Mount Holyoke College

> The use of web data in small area estimation of attitudes towards climate change
Moretti, A., Utrecht University; Camilla Salvatore, Utrecht University

> Improving estimates on housing wealth with survey and administrative data
Porreca, E., Bank of Italy; Benedetti, R., University of Chieti-Pescara; Neri, A., Bank of Italy; Ranalli, M. G., University of Perugia

> Distributional wealth accounts
Blatnik, N., European Central Bank; Celislami, E., European Central Bank

> Statistical matching approaches to investigate the relationship between household income, consumption and wealth in Italy
D’Orazio, M., ISTAT; Donatiello, G., ISTAT; Loschiavo, D., Bank of Italy; Neri, A., Bank of Italy, Tullio, F., Bank of Italy

> Consistent estimation of linear regression models from different data sources with many variables in common
Hirukawa, M., Ryukoku University

> Including the spatial balance criterium into the estimator
Pantalone, F., University of Southampton; Benedetti, R., “G. d’Annunzio” University; Piersimoni, F., ISTAT

> Producing representative data in a monthly employment panel in Germany: Challenges and approaches in a longitudinal context
Seesing-Coelho, C., Federal Statistical Office of Germany & University of Hamburg; Jaeger, C., Federal Statistical Office of Germany; Preising, M., Federal Statistical Office of Germany

> STATENT Flash: Rapid estimates for the swiss structural business statistics
Chalimourda, A., Swiss Federal Statistical Office; Vallon, N., Swiss Federal Statistical Office; Nedyalkova, D.; Swiss Federal Statistical Office; Assoulin, D., Swiss Federal Statistical Office

> On some composite estimators for domain estimation under systematic sampling design
Yadav, G., Banaras Hindu University

Mauro Bruno; ISTAT
Frauke Kreuter; University of Maryland
Monica Pratesi; University of Pisa
Changbao Wu; University of Waterloo