Salīdzināt metodes

	Tobita cenzētās regresijas modelis	Logistiskā regresija	Negatīvā binomiālā regresija	Parastā mazāko kvadrātu (OLS) regresija	Kvantīļu regresija
Nozare	Ekonometrija	Pētniecības statistika	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Process / pipeline	Regression model	Regression model	Regression model
Izcelsmes gads	1958	1958	2011	2019	1978
Autors	James Tobin	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Tips	Censored regression (limited dependent variable)	Method	Generalized linear model for count data	Linear regression	Conditional quantile regression
Pirmavots	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Citi nosaukumi	censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon)	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Saistītās	4	3	4	5	5
Kopsavilkums	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.