مقایسهٔ روشها
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| رگرسیون لجستیک چندجمله ای مقاوم× | مدل خطی تعمیمیافته (GLM)× | |
|---|---|---|
| حوزه | آمار | آمار |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 2001 (robust GLM); 1970s–1980s (multinomial logistic regression) | 1972 |
| پدیدآور≠ | Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression) | John A. Nelder & Robert W. M. Wedderburn |
| نوع≠ | Robust classification model | Regression framework |
| منبع بنیادین≠ | Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030. DOI ↗ | 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 ↗ |
| نامهای دیگر | robust polychotomous logistic regression, outlier-resistant multinomial regression, robust nominal logistic regression, M-estimation multinomial logistic regression | GLM, generalized regression, exponential family regression, link-function model |
| مرتبط≠ | 5 | 6 |
| خلاصه≠ | Robust multinomial logistic regression extends the standard multinomial logit model to handle outliers, influential observations, and mild misspecification of the response distribution. It replaces the conventional maximum likelihood score equations with bounded influence functions (M-estimation) or pairs maximum likelihood with sandwich variance estimators, so that a small fraction of anomalous cases cannot distort the estimated log-odds ratios across outcome categories. | 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. |
| ScholarGateمجموعهداده ↗ |
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