Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustā loģistiskā regresija× | MM-Estimator× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2001 | 1987 |
| Autors≠ | Cantoni & Ronchetti (2001); Bondell (2008) | Victor J. Yohai |
| Tips≠ | Robust generalized linear model (binary outcome) | Robust linear regression |
| Pirmavots≠ | Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ |
| Citi nosaukumi | robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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). | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. |
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