Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Negativně binomická regrese× | Regrese metodou ordinárních nejmenších čtverců (OLS)× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2011 | 2019 |
| Tvůrce≠ | Hilbe (textbook treatment); generalized linear model framework | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Generalized linear model for count data | Linear regression |
| Původní zdroj≠ | 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 |
| Další názvy≠ | NB regression, NB2 regression, negatif binom regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Příbuzné≠ | 4 | 5 |
| Shrnutí≠ | 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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