Sammenlign metoder

	Heckman-modellen for utvalgsseleksjon (Heckit / Tobit type II)	Logistisk regresjon	Kvantilregresjon
Fagfelt	Økonometri	Forskningsstatistikk	Økonometri
Familie	Regression model	Process / pipeline	Regression model
Opprinnelsesår	1979	1958	1978
Opphavsperson	James J. Heckman	David Roxbee Cox	Koenker & Bassett
Type	Two-step sample selection model	Method	Conditional quantile regression
Opprinnelig kilde	Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153–161.	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.
Alias	heckit, tobit type II, sample selection model, Heckman Seçim Modeli (Heckit / Tobit II)	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
Relaterte	4	3	5
Sammendrag	The Heckman selection model, introduced by James J. Heckman in 1979, is a two-step model that corrects sample selection bias when the outcome is only observed for a non-random subset of cases. A probit selection equation models who is observed, and the outcome equation then corrects for the resulting bias using the inverse Mills ratio.	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.