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| Tobit censurerad regressionsmodell× | Logistisk regression× | Vanligaste minsta kvadratmetoden (OLS) Regression× | |
|---|---|---|---|
| Ämnesområde≠ | Ekonometri | Forskningsstatistik | Ekonometri |
| Familj≠ | Regression model | Process / pipeline | Regression model |
| Ursprungsår≠ | 1958 | 1958 | 2019 |
| Upphovsperson≠ | James Tobin | David Roxbee Cox | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Censored regression (limited dependent variable) | Method | Linear regression |
| Ursprungskälla≠ | Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36. 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 |
| Alias≠ | censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon) | logit model, binomial logistic regression, LR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Närliggande≠ | 4 | 3 | 5 |
| Sammanfattning≠ | 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. | 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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