Сравнение методов

	Модель регрессии с цензурированием Тобіта	Логистическая регрессия	Регрессия отрицательного биномиального распределения
Область	Эконометрика	Статистика исследований	Эконометрика
Семейство	Regression model	Process / pipeline	Regression model
Год появления	1958	1958	2011
Автор метода	James Tobin	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework
Тип	Censored regression (limited dependent variable)	Method	Generalized linear model for count data
Основополагающий источник	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.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Другие названия	censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon)	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu
Связанные	4	3	4
Сводка	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.	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.