Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Sn un Qn robustie mēroga novērtētāji× | Kvantīļu regresija× | |
|---|---|---|
| Nozare≠ | Statistika | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1993 | 1978 |
| Autors≠ | Rousseeuw & Croux | Koenker & Bassett |
| Tips≠ | Robust scale estimator | Conditional quantile regression |
| Pirmavots≠ | Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Citi nosaukumi≠ | Sn estimator, Qn estimator, Rousseeuw-Croux scale estimators, robust scale estimation | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Sn and Qn are robust estimators of scale (spread) proposed by Rousseeuw and Croux (1993) as alternatives to the median absolute deviation (MAD). Both attain a 50% breakdown point while delivering higher statistical efficiency than MAD, so they measure dispersion accurately even when the data contain outliers. | 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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