ScholarGate
Assistant

Comparer des méthodes

Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.

Modèle de régression censurée de Tobit×Régression logistique×Régression quantile×
DomaineÉconométrieStatistiques de rechercheÉconométrie
FamilleRegression modelProcess / pipelineRegression model
Année d'origine195819581978
Auteur d'origineJames TobinDavid Roxbee CoxKoenker & Bassett
TypeCensored regression (limited dependent variable)MethodConditional quantile regression
Source fondatriceTobin, 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
Apparentées435
RésuméThe 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.
ScholarGateJeu de données
  1. v1
  2. 1 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

Aller à la recherche Télécharger les diapositives

ScholarGateComparer des méthodes: Tobit Model · Logistic Regression · Quantile Regression. Consulté le 2026-06-18 sur https://scholargate.app/fr/compare