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| Μοντέλο Επιλογής Δείγματος Heckman (Heckit / Tobit Τύπου II)× | Λογιστική Παλινδρόμηση× | Παλινδρόμηση Ελαχίστων Τετραγώνων (OLS)× | |
|---|---|---|---|
| Πεδίο≠ | Οικονομετρία | Ερευνητική Στατιστική | Οικονομετρία |
| Οικογένεια≠ | Regression model | Process / pipeline | Regression model |
| Έτος προέλευσης≠ | 1979 | 1958 | 2019 |
| Δημιουργός≠ | James J. Heckman | David Roxbee Cox | Wooldridge (textbook treatment); classical least squares |
| Τύπος≠ | Two-step sample selection model | Method | Linear regression |
| Θεμελιώδης πηγή≠ | Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153–161. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Εναλλακτικές ονομασίες≠ | heckit, tobit type II, sample selection model, Heckman Seçim Modeli (Heckit / Tobit II) | logit model, binomial logistic regression, LR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Συναφείς≠ | 4 | 3 | 5 |
| Σύνοψη≠ | 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. | 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). |
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