Comparar métodos

	Estimación por Máxima Verosimilitud	Regresión Logística
Campo	Estadística	Estadística para la investigación
Familia	Regression model	Process / pipeline
Año de origen	1922	1958
Autor original	R. A. Fisher	David Roxbee Cox
Tipo	Parametric point estimator	Method
Fuente seminal	Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood	logit model, binomial logistic regression, LR
Relacionados	4	3
Resumen	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.