Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Mazākās apgrieztās kvadrātiskās kļūdas (LTS) regresija× | M-novērtēji (Robustā regresija)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1984 | 2009 |
| Autors≠ | Peter J. Rousseeuw | Peter J. Huber |
| Tips | Robust linear regression | Robust linear regression |
| Pirmavots≠ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ |
| Citi nosaukumi≠ | LTS, least trimmed squares regression, trimmed least squares, robust regression | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers. | M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit. |
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