Statistical Methods for Data Analysis and Decision Sciences

This volume on statistical methods for data analysis and decision sciences showcases the interplay between statistics and data science, reflecting a broad spectrum of data-driven challenges, innovative approaches, methodological developments and applications.

This volume on statistical methods for data analysis and decision sciences showcases the interplay between statistics and data science, reflecting a broad spectrum of data-driven challenges, innovative approaches, methodological developments and applications. It gathers peer-reviewed short papers presented at the 3rd Conference of the Statistics and Data Science Group of the Italian Statistical Society, SDS 2025, held in Milan, Italy, April 2-3, 2025. The papers cover a wide variety of topics focused on modeling and analyzing complex data, including structured, non-structured and mixed data, offering fresh perspectives tailored to diverse research goals. The contributions propose innovative methods and approaches in causal inference, sampling and big data, Bayesian methods, functional data analysis, unsupervised learning, robust statistics and penalized regression, spatio-temporal methods, clustering, time series analysis, text analysis and image processing. A wide range of applications in several areas are presented, including environmental issues and sustainability, earth and geohazards, official statistics, social issues and inequality, health informatics, medicine, health and well-being, statistical process monitoring, financial statistics and econometrics, and artificial intelligence, addressing matters of particular relevance for sustainable development, including Sustainable Development Goals (SDGs) 1, 3, 5, 7, and 13, among others.