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| 강건 다항 로지스틱 회귀분석× | 일반화 선형 모형 (GLM)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2001 (robust GLM); 1970s–1980s (multinomial logistic regression) | 1972 |
| 창시자≠ | Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression) | John A. Nelder & Robert W. M. Wedderburn |
| 유형≠ | Robust classification model | Regression framework |
| 원전≠ | 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 ↗ |
| 별칭 | 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 |
| 관련≠ | 5 | 6 |
| 요약≠ | 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. |
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