SAE & NONTRADITIONAL DATA
SAE & NONTRADITIONAL DATA
We are often interested in estimating quantities or constructing social indicators at the “small-area” level, for example, at the provincial or sub-regional level. Traditional probability sample surveys are not designed to provide reliable estimates at this level. One solution is to use Small Area Estimation (SAE) techniques.
I am part of the SAE group led by Angelo Moretti at UU, where we develop innovative SAE methods and study how non-traditional data can be incorporated into the SAE framework.
Check my publication and coference presentation list for more insight on my work!
Markwitz, R., Moretti, A., & Salvatore, C. (2026). A Spatial Fay-Herriot Model when the Auxiliary Variables are based on Non-traditional Data. arXiv preprint arXiv:2608.09626.
Salvatore C. & Moretti A. (2024). The Use of New Data Sources in Small Area Estimation of Attitudes towards Climate Change. The Survey Statistician, Vol. 90, 30–37, Link