Σύγκριση μεθόδων

	Εκτίμηση Μικρών Περιοχών (Μοντέλο Fay-Herriot)	Στάθμιση και Βαθμονόμηση Ερευνών
Πεδίο	Μεθοδολογία Επισκοπήσεων	Μεθοδολογία Επισκοπήσεων
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	1979	2010
Δημιουργός	Robert Fay & Roger Herriot	Sharon Lohr
Τύπος	Model-based survey estimator	Estimation adjustment procedure
Θεμελιώδης πηγή	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.	Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5
Εναλλακτικές ονομασίες	SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini	Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)
Συναφείς	2	3
Σύνοψη	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.	Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.