3 papers
stat.AP2025
Density Estimation from Aggregated Data with Integrated Auxiliary Information: Estimating Population Densities with Geospatial Data
Michael Mühlbauer, Timo Schmid
Density estimation for geospatial data ideally relies on precise geocoordinates, typically defined by longitude and latitude. However, such detailed information is often unavailabl…
stat.ME2024
Small area prediction of counts under machine learning-type mixed models
Nicolas Frink, Timo Schmid
This paper proposes small area estimation methods that utilize generalized tree-based machine learning techniques to improve the estimation of disaggregated means in small areas us…
stat.ME2024
Small area estimation with generalized random forests: Estimating poverty rates in Mexico
Nicolas Frink, Timo Schmid
Identifying and addressing poverty is challenging in administrative units with limited information on income distribution and well-being. To overcome this obstacle, small area esti…