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Methodological and Applied Statistics and Demography II

SIS 2024, Short Papers, Solicited Sessions

Alessio Pollice (Hrsg.), Paolo Mariani (Hrsg.)

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ca. 267,49 (Lieferbar ab 03. April 2026)

Springer Nature Switzerland img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik

Beschreibung

This book of peer-reviewed short papers on methodological and applied statistics and demography is the second of four volumes from the 52nd Scientific Meeting of the Italian Statistical Society (SIS 2024), held in Bari, Italy, on June 17-20, 2024. It features invited contributions presented in the Solicited Sessions.

The volumes address a large number of topics and applications of current interest. The topics covered include, but are not limited to, statistical theory and methods, sampling theory, Bayesian statistics, statistical modeling, computational statistics, classification, data analysis, gender statistics and applied statistics. The applications reflect new analyses in a wide variety of fields, including demography, psychometrics, education, business, economics, finance, law, and other social sciences and humanities, epidemiology, the life and health sciences as well as the environmental and natural sciences and engineering. This variety also demonstrates the important role of statistical science in addressing the societal and environmental challenges of sustainable development.

One of the aims of the Italian Statistical Society (SIS) is to promote scientific activities for the development of statistical sciences. Its biennial international Scientific Meeting represents the Society’s largest event which brings together national and international researchers and professionals to exchange ideas and discuss recent advances and developments in theoretical and applied statistics.

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Schlagwörter

Statistical Methods, Applied Statistics, Computational Statistics, Bayesian Inference, Classification, Demography, Statistical Modeling, Statistical Theory, Sampling, Data Analysis, Data Science