مقایسهٔ روش‌ها

	وزن‌دهی و کالیبراسیون پیمایش	چندگانه سازی	برآورد برای مناطق کوچک (مدل فی-هریوت)
حوزه	روش‌شناسی پیمایش	آمار	روش‌شناسی پیمایش
خانواده	Process / pipeline	Process / pipeline	Regression model
سال پیدایش	2010	1987	1979
پدیدآور	Sharon Lohr	Donald B. Rubin	Robert Fay & Roger Herriot
نوع	Estimation adjustment procedure	Missing-data handling procedure	Model-based survey estimator
منبع بنیادین	Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5	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.
نام‌های دیگر	Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)	SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini
مرتبط	3	1	2
خلاصه	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.	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.