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| टोबिट सेंसरित प्रतिगमन मॉडल× | ऋणात्मक द्विपद समाश्रयण (Negative Binomial Regression)× | साधारण न्यूनतम वर्ग (OLS) समाश्रयण× | |
|---|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model | Regression model |
| उद्भव वर्ष≠ | 1958 | 2011 | 2019 |
| प्रवर्तक≠ | James Tobin | Hilbe (textbook treatment); generalized linear model framework | Wooldridge (textbook treatment); classical least squares |
| प्रकार≠ | Censored regression (limited dependent variable) | Generalized linear model for count data | Linear regression |
| मौलिक स्रोत≠ | Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| उपनाम≠ | censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon) | NB regression, NB2 regression, negatif binom regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| संबंधित≠ | 4 | 4 | 5 |
| सारांश≠ | 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. | 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). |
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