Võrdle meetodeid

	Küsitluse kaalutamine ja kalibreerimine	Multiple Imputation
Valdkond	Küsitlusmetoodika	Statistika
Perekond	Process / pipeline	Process / pipeline
Tekkeaasta	2010	1987
Looja	Sharon Lohr	Donald B. Rubin
Tüüp	Estimation adjustment procedure	Missing-data handling procedure
Algallikas	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.
Rööpnimetused	Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
Seotud	3	1
Kokkuvõte	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.