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| Негативно-биномна регресия× | Квантилна регресия× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2011 | 1978 |
| Създател≠ | Hilbe (textbook treatment); generalized linear model framework | Koenker & Bassett |
| Тип≠ | Generalized linear model for count data | Conditional quantile regression |
| Основополагащ източник≠ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Други названия | NB regression, NB2 regression, negatif binom regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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