ScholarGate
Assistente

Confronta i metodi

Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.

Multiple Imputation×Stima per Piccole Aree (Modello di Fay-Herriot)×
CampoStatisticaMetodologia delle indagini
FamigliaProcess / pipelineRegression model
Anno di origine19871979
IdeatoreDonald B. RubinRobert Fay & Roger Herriot
TipoMissing-data handling procedureModel-based survey estimator
Fonte seminaleRubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley. DOI ↗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. DOI ↗
AliasMICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini
Correlati12
SintesiMultiple 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.
ScholarGateInsieme di dati
  1. v1
  2. 2 Fonti
  3. PUBLISHED
  1. v1
  2. 1 Fonti
  3. PUBLISHED

Vai alla ricerca Scarica le diapositive

ScholarGateConfronta i metodi: Multiple Imputation · Small Area Estimation. Consultato il 2026-06-18 da https://scholargate.app/it/compare