Methoden vergleichen
Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.
| Robuste Logistische Regression× | Quantile Regression× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2001 | 1978 |
| Urheber≠ | Cantoni & Ronchetti (2001); Bondell (2008) | Koenker & Bassett |
| Typ≠ | Robust generalized linear model (binary outcome) | Conditional quantile regression |
| Wegweisende Quelle≠ | Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Aliasnamen≠ | robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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). | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateDatensatz ↗ |
|
|