השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| רגרסיה לוגיסטית מולטינומית רובסטית× | מודל לינארי מוכלל (GLM)× | |
|---|---|---|
| תחום | סטטיסטיקה | סטטיסטיקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 2001 (robust GLM); 1970s–1980s (multinomial logistic regression) | 1972 |
| הוגה השיטה≠ | Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression) | John A. Nelder & Robert W. M. Wedderburn |
| סוג≠ | Robust classification model | Regression framework |
| מקור מכונן≠ | 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 ↗ |
| כינויים | 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 |
| קשורות≠ | 5 | 6 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
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