Advanced Methods in Statistics, Data Science and Related Applications : SIS 2022, Caserta, Italy, June 22–24 / / edited by Matilde Bini, Antonio Balzanella, Lucio Masserini, Rosanna Verde

• Autore: Bini Matilde
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (321 pages)
• Disciplina: 001.422; 005.7
• Soggetto topico: Quantitative research; Machine learning; Data Analysis and Big Data; Machine Learning
• Altri autori: BalzanellaAntonio MasseriniLucio VerdeRosanna
• Nota di contenuto: C. Marini, and V. Nicolardi, Administrative database and official statistics: an IT and Statistical procedure -- F. Mariani, M. Ciommi, M. C. Recchioni, Giuseppe Ricciardo Lamonica and Francesco Maria Chelli, Working with Non-Compensatory Composite Indicators: A Case Study Based on SDG for Mediterranean Countries -- D. Bondonio and P. Chirico, Intertemporal statistical matching for causal inference in the context of multivariate time-series data -- C. Rosanna, G. M. Gabriella and Z. Emma, Scaling UX-AI products: CFA & PLS-SEM comparison -- L. Pagani, M. C. Zanarotti and A. Habus, The construction of a Heat Vulnerability Index by means of the Composite Indicator approach: a case study for Friuli Venezia Giulia Region, Italy -- C. Pangallo, Oliviero Casacchia e Corrado Polli, The manual, communicative and quantitative abilities of native and foreign workers according to their level of education in Italy -- E. Dzuverovic and E. Otranto, Nonlinear HAR Models and Nonlinear Least Squares: Asymptotic Properties -- P. Quatto and E. Ripamonti, Measuring strength of randomized clinical trials -- A. Bianchino, Armando d’Aniello, Daniela Fusco, Improving administrative data quality on tourism using Big Data -- R. Fontana and F. Rapallo, Robust designs against data loss: A general approach -- S. D. Tomarchio, A. Punzo and A. Maruotti, Matrix-variate hidden Markov models: an application to employment data -- F. Bitonti, Integrating structuralism and diffusionism to explain the new Italian emigration -- F. Bitonti1, A. Mazza, Spatial explorative analysis of thyroid cancer in Sicilian volcanic areas -- I. Sciascia, Adjusted calibration estimators for sparse spatial data -- N. Trendafilov and M. Gallo and V. Simonacci and V. Todorov, Discrimination via principal components -- G. Greca, G. Cinquegrana and G. Fosco, A regional analysis of the efficiency by energy producers in Italy -- M. Scioni and P. Annoni, An Alternative Aggregation Function for the UNDP Human Development Index -- F. Attili and M. Costa, Decomposing inequality after asymmetric shocks: an analysis of Italian household consumption -- D. Fusco, M. Antonietta Liguori, V. Moretti, F. G. Truglia, Spatial statistics analysis using microdata: an application agricultural sector -- I. Primerano, G. Giordano, M. Prosperina Vitale, Exploring factors affecting the evaluation of online learning services. Evidence from a social science bachelor’s degree -- M. Zannella, A. De Rose, E. Aloè, M. Corsi, Paid and Unpaid Work in Pandemic Times. A Study on the Division of Household Labour and the Subjective Well-being of Working Mothers in Italy -- G. Bove, Measures of interrater agreement for quantitative scales based on the standard deviation -- D. Giuliani, M. Michela Dickson, F. Santi, G. Espa, An empirical tool to classify industries by regional concentration and spatial polarization -- B. Guindani, D. Ardagna and A. Guglielmi, Bayesian optimization for cloud resource management through machine learning.
• Sommario/riassunto: This book contains a selection of the improved contributions submitted by participants at the conference of the Italian Statistical Society - SIS 2022 held in Caserta 22-24 June 2022. The scientific community of Italian statistics, which gathers around the SIS, is paying particular attention to the development of statistical techniques increasingly oriented toward the processing of large data, mainly, of complex data. The main goal is to provide the analysis of the data and the interpretability of the obtained results, with a view to decision support and the reliability of the data outcomes. The aim of this volume is to show some of the most relevant contributions of statistical and data analysis methods in preserving the quality of the information to be processed, especially when it comes from different, often non-official sources; as well as in the extraction of knowledge from complex data (textual, network, unstructured and multivalue) and in the explicability of results. Data Science today represents a broad domain of knowledge development from data, where statistical and data analysis methods can make an important contribution in the different domains where data management and processing are required. This volume is addressed to researchers but also to Ph.D. and MSc students in the field of Statistics and Data Science to acquaint them with some of the most recent developments towards which statistical research is orienting, in prevalence in Italy.
• Titolo autorizzato: Advanced Methods in Statistics, Data Science and Related Applications
• ISBN: 3-031-65699-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910897974903321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Proceedings in Mathematics & Statistics, . 2194-1017 ; ; 467