Сравнение методов

	Механизмы пропущенных данных: MCAR, MAR и MNAR	Множественная импутация
Область	Статистика	Статистика
Семейство	Process / pipeline	Process / pipeline
Год появления	1976	1987
Автор метода	Donald Rubin	Donald B. Rubin
Тип	Diagnostic / classification framework	Missing-data handling procedure
Основополагающий источник	Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.
Другие названия	Missing Data Typology, Rubin's Missing Data Framework, Missingness Mechanisms, Kayıp Veri Mekanizmaları	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
Связанные	3	1
Сводка	Missing data mechanisms, introduced by Donald Rubin in 1976, provide a formal taxonomy for classifying why observations are absent from a dataset. The three categories — Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) — describe the relationship between the probability of missingness and the observed or unobserved values. Identifying the correct mechanism is essential because it determines which analytical strategies preserve valid and unbiased inference.	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.