Methoden vergleichen

	Small Area Estimation (Fay-Herriot-Modell)	Geschichtete Stichprobenziehung
Fachgebiet	Umfragemethodik	Umfragemethodik
Familie	Regression model	Process / pipeline
Entstehungsjahr	1979	1977
Urheber	Robert Fay & Roger Herriot	William G. Cochran
Typ	Model-based survey estimator	Probability-based survey sampling design
Wegweisende Quelle	Fay, R. E., & Herriot, R. A. (1979). Estimates of income for small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, 74(366), 269–277.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Aliasnamen	SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Verwandt	2	2
Zusammenfassung	Small Area Estimation (SAE) refers to statistical techniques that produce reliable estimates for subpopulations — geographical regions, demographic groups, or administrative units — where direct survey samples are too sparse to yield acceptable precision. The Fay-Herriot model, introduced by Robert Fay and Roger Herriot in 1979, is the canonical area-level SAE model. It supplements weak direct survey estimates with auxiliary covariate information through an empirical Bayes or BLUP framework, substantially reducing mean squared error for small domains.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.