विधियों की तुलना करें

	अधिकतम संभावना आकलन	MICE
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	1922	2011
प्रवर्तक	R. A. Fisher	Stef van Buuren & Karin Groothuis-Oudshoorn
प्रकार	Parametric point estimator	Iterative multiple imputation algorithm
मौलिक स्रोत	Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.	van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.
उपनाम	MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood	Fully Conditional Specification, Sequential Regression Multivariate Imputation, Chained Equations Imputation, Zincirleme Denklemlerle Çoklu Atama
संबंधित	4	3
सारांश	Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures.	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.