방법 비교

	결측치가 있는 MCMC	Multiple Imputation
분야	베이지안	통계학
계열	Bayesian methods	Process / pipeline
기원 연도	1987	1987
창시자	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin	Donald B. Rubin
유형	Bayesian computational method	Missing-data handling procedure
원전	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.
별칭	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
관련	6	1
요약	MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fully accounts for uncertainty introduced by the missingness.	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.