Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная регрессия отрицательного биномиального распределения× | Обобщенная линейная модель (GLM)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2000s–2011 | 1972 |
| Автор метода≠ | Hilbe, J. M.; Zeileis, A. et al. | John A. Nelder & Robert W. M. Wedderburn |
| Тип≠ | Count regression with robust inference | Regression framework |
| Основополагающий источник≠ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158 | 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 ↗ |
| Другие названия | robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression | GLM, generalized regression, exponential family regression, link-function model |
| Связанные | 6 | 6 |
| Сводка≠ | Robust Negative Binomial Regression models overdispersed count outcomes using the negative binomial distribution while protecting coefficient inference against misspecification of the variance function. It pairs maximum-likelihood estimation of the mean and dispersion parameters with sandwich (Huber-White) standard errors, yielding valid tests even when the assumed variance structure is only approximately correct. | 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. |
| ScholarGateНабор данных ↗ |
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