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| Robust logistisk regression× | Kvantilregression× | |
|---|---|---|
| Ämnesområde≠ | Statistik | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2001 | 1978 |
| Upphovsperson≠ | Cantoni & Ronchetti (2001); Bondell (2008) | Koenker & Bassett |
| Typ≠ | Robust generalized linear model (binary outcome) | Conditional quantile regression |
| Ursprungskälla≠ | 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 ↗ |
| Alias≠ | robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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. |
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