Σύγκριση μεθόδων

	Μοντέλο Παλινδρόμησης Tobit με Λογοκρισία	Λογιστική Παλινδρόμηση	Παλινδρόμηση Ποσοστημορίων
Πεδίο	Οικονομετρία	Ερευνητική Στατιστική	Οικονομετρία
Οικογένεια	Regression model	Process / pipeline	Regression model
Έτος προέλευσης	1958	1958	1978
Δημιουργός	James Tobin	David Roxbee Cox	Koenker & Bassett
Τύπος	Censored regression (limited dependent variable)	Method	Conditional quantile regression
Θεμελιώδης πηγή	Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Εναλλακτικές ονομασίες	censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon)	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
Συναφείς	4	3	5
Σύνοψη	The 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.