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Modelo de regresión censurada de Tobit×Regresión Logística×Regresión Cuantílica×
CampoEconometríaEstadística para la investigaciónEconometría
FamiliaRegression modelProcess / pipelineRegression model
Año de origen195819581978
Autor originalJames TobinDavid Roxbee CoxKoenker & Bassett
TipoCensored regression (limited dependent variable)MethodConditional quantile regression
Fuente seminalTobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36. DOI ↗Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Aliascensored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon)logit model, binomial logistic regression, LRconditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados435
ResumenThe Tobit model is a regression for outcomes that are censored at a threshold, estimating the relationship by maximum likelihood. Introduced by James Tobin in 1958, it addresses the pile-up of observations at a limit (typically zero) in data such as spending, wages, or duration.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.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
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ScholarGateComparar métodos: Tobit Model · Logistic Regression · Quantile Regression. Recuperado el 2026-06-18 de https://scholargate.app/es/compare