Salīdzināt metodes

	Meklēto datu mehānismi: MCAR, MAR un MNAR	MICE
Nozare	Statistika	Statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1976	2011
Autors	Donald Rubin	Stef van Buuren & Karin Groothuis-Oudshoorn
Tips	Diagnostic / classification framework	Iterative multiple imputation algorithm
Pirmavots	Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.	van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.
Citi nosaukumi	Missing Data Typology, Rubin's Missing Data Framework, Missingness Mechanisms, Kayıp Veri Mekanizmaları	Fully Conditional Specification, Sequential Regression Multivariate Imputation, Chained Equations Imputation, Zincirleme Denklemlerle Çoklu Atama
Saistītās	3	3
Kopsavilkums	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.	Multivariate Imputation by Chained Equations (MICE) is an iterative procedure for handling missing data in multivariate datasets. Introduced by Stef van Buuren and Karin Groothuis-Oudshoorn through the R package mice (2011), the algorithm fills each missing variable using a separate regression model conditioned on all other variables, cycling through variables repeatedly until the imputed values converge. The result is m completed datasets that are analysed separately and combined using Rubin's rules.