Sammenlign metoder

	EM-algoritmen	Multiple Imputation
Fagområde	Statistik	Statistik
Familie	Machine learning	Process / pipeline
Oprindelsesår	1977	1987
Ophavsperson	Dempster, Laird & Rubin	Donald B. Rubin
Type	Iterative optimization algorithm	Missing-data handling procedure
Oprindelig kilde	Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.
Aliasser	EM, Expectation-Maximization, Maximum Likelihood via Incomplete Data, BM Algoritması	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
Relaterede	2	1
Resumé	The Expectation-Maximization (EM) algorithm is an iterative optimization procedure for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models with latent variables or missing data. Introduced by Dempster, Laird, and Rubin in their landmark 1977 paper, EM alternates between computing the expected complete-data log-likelihood (E-step) and maximizing it with respect to the parameters (M-step), guaranteeing monotone non-decreasing likelihood at each iteration.	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.