Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Робастний дискримінантний аналіз× | Робастна логістична регресія× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1997 | 2001 |
| Автор методу≠ | Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA) | Cantoni & Ronchetti (2001); Bondell (2008) |
| Тип≠ | Robust classification / discriminant analysis | Robust generalized linear model (binary outcome) |
| Основоположне джерело≠ | Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143. DOI ↗ | Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗ |
| Інші назви | robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi | robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001). | Robust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008). |
| ScholarGateНабір даних ↗ |
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