Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Γενικευμένο Γραμμικό Μοντέλο (GLM)× | Ανάλυση Παλινδρόμησης Αρνητικού Διωνύμου× | |
|---|---|---|
| Πεδίο≠ | Στατιστική | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 1972 | 2011 |
| Δημιουργός≠ | John A. Nelder & Robert W. M. Wedderburn | Hilbe (textbook treatment); generalized linear model framework |
| Τύπος≠ | Regression framework | Generalized linear model for count data |
| Θεμελιώδης πηγή≠ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Εναλλακτικές ονομασίες≠ | GLM, generalized regression, exponential family regression, link-function model | NB regression, NB2 regression, negatif binom regresyonu |
| Συναφείς≠ | 6 | 4 |
| Σύνοψη≠ | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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