Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Teila-Senas novērtētājs× | Mazākās apgrieztās kvadrātiskās kļūdas (LTS) regresija× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1968 | 1984 |
| Autors≠ | Henri Theil (1950); P. K. Sen (1968) | Peter J. Rousseeuw |
| Tips | Robust linear regression | Robust linear regression |
| Pirmavots≠ | Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| Citi nosaukumi | Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%. | 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. |
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