Сравнение на методи

	Модел на Тобит за цензурирани регресии	Негативно-биномна регресия	Квантилна регресия
Област	Иконометрия	Иконометрия	Иконометрия
Семейство	Regression model	Regression model	Regression model
Година на възникване	1958	2011	1978
Създател	James Tobin	Hilbe (textbook treatment); generalized linear model framework	Koenker & Bassett
Тип	Censored regression (limited dependent variable)	Generalized linear model for count data	Conditional quantile regression
Основополагащ източник	Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Други названия	censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon)	NB regression, NB2 regression, negatif binom regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Свързани	4	4	5
Резюме	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.	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.