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stat.ME2026
Diagnostics-guided variance-inflated Fay-Herriot estimation from non-probability samples
Andrius Čiginas
Non-probability data sources are increasingly considered in small area estimation, but inverse probability weighting (IPW) gives model-dependent domain estimators whose reliability…
stat.ME2026
Toward design-based inference for data integration
Andrius Čiginas, Ieva Burakauskaitė, Jae Kwang Kim
Integrating non-probability samples into finite-population inference typically requires modeling unknown selection probabilities under a missing-at-random (MAR) assumption that is…
stat.ME2026
Small area estimation using incomplete auxiliary information
Donatas Šlevinskas, Ieva Burakauskaitė, Andrius Čiginas
Auxiliary information is increasingly available from administrative and other data sources, but it is often incomplete and of non-probability origin. We propose a two-step small ar…