1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Design-based composite estimation of small proportions in small domains
Andrius Čiginas
Traditional direct estimation methods are not efficient for domains of a survey population with small sample sizes. To estimate the domain proportions, we combine the direct estima…
A solution in small area estimation problems
Andrius Čiginas, Tomas Rudys
We present a new method in problems where estimates are needed for finite population domains with small or even zero sample sizes. In contrast to known estimation methods, an auxil…
On the asymptotic normality of finite population L-statistics
Andrius Čiginas
We give sufficient conditions for the asymptotic normality of linear combinations of order statistics (L-statistics) in the case of simple random samples without replacement. In th…