Finite population inference remains a core challenge in statistics, spanning the full methodological pipeline from sampling design to estimation, through to the integration of multiple data sources. This session brings together researchers working at the methodological frontier of finite population inference, covering complex sampling design, model-assisted and machine-learning-based estimation, small area estimation, functional measurement error, and the integration of survey, administrative, and macroeconomic data to produce coherent and timely estimates. These methodological advancements are particularly relevant to a modern approach to descriptive inference for finite populations, in a context in which the role of sample surveys faces new challenges, such as declining response rates and substantial measurement error, alongside new opportunities arising from novel data sources and machine learning methods. These methods are motivated by genuine survey data problems, with particular emphasis on the measurement of wealth and poverty, an area in which reliable estimates of descriptive parameters beyond the mean and at fine geographical levels are increasingly in demand.
Invited authors come from academia (University of Bologna, University of Helsinki, University of Ottawa, University of Technology Sydney) and from national and international statistical research institutions (NORC at the University of Chicago, Bank of Italy, European Central Bank, Badan Pusat Statistik-Statistics Indonesia). Confirmed presenters and the discussant are drawn from Europe, North America, and Australia, and reflect a good balance in terms of gender and years of experience within the statistical profession.

Presentations:
An Integrated Optimization Framework for Complex Survey Design
F. Jay Breidt, Benjamin Reist, NORC at the University of Chicago (US)

Agnostic model assisted estimation with machine learning
David Haziza, University of Ottawa (Canada)

Data integration for estimating the distribution of wealth
Ilja Kristian Kavonius, University of Helsinki (Finland) and European Central Bank, Andrea Neri, Bank of Italy

The Wealth Side of Poverty: Regional Estimates and Spatial Patterns
Alessia Broka, Silvia De Nicolò, Maria Rosaria Ferrante, University of Bologna (Italy)

Integrating Auxiliary Data with Errors for Small Area Estimation of Wealth of Indonesian Households
Ika Yuni Wulansari (Badan Pusat Statistik-Statistics Indonesia) and James J Brown (University of Technology Sydney, Australia)

Prof. Gaia Bertarelli, Discussant