Σύγκριση μεθόδων

	Πολλαπλή Στατιστική Πληροφόρηση	Εκτίμηση Μικρών Περιοχών (Μοντέλο Fay-Herriot)
Πεδίο	Στατιστική	Μεθοδολογία Επισκοπήσεων
Οικογένεια	Process / pipeline	Regression model
Έτος προέλευσης	1987	1979
Δημιουργός	Donald B. Rubin	Robert Fay & Roger Herriot
Τύπος	Missing-data handling procedure	Model-based survey estimator
Θεμελιώδης πηγή	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.	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.
Εναλλακτικές ονομασίες	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)	SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini
Συναφείς	1	2
Σύνοψη	Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models.	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.