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| Robusztus Mahalanobis-távolság× | Legkisebb Nyesett Négyzetes (LTS) Regresszió× | |
|---|---|---|
| Tudományterület | Statisztika | Statisztika |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 1990 | 1984 |
| Megalkotó≠ | Rousseeuw & Van Zomeren (robust distance); Filzmoser, Garrett & Reimann (multivariate outlier detection) | Peter J. Rousseeuw |
| Típus≠ | Robust multivariate outlier detection | Robust linear regression |
| Alapmű≠ | Rousseeuw, P. J. & Van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association, 85(411), 633-639. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| Alternatív nevek≠ | MCD Mahalanobis distance, robust mahalanobis, minimum covariance determinant distance, Robust Mahalanobis Uzaklığı | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | Robust Mahalanobis Distance flags multivariate outliers by measuring how far each observation lies from the centre of the data using a robust covariance estimate. It builds on the robust-distance framework of Rousseeuw and Van Zomeren (1990) and the multivariate outlier-detection approach of Filzmoser, Garrett and Reimann (2005), replacing the classical mean and covariance with the Minimum Covariance Determinant (MCD) estimate so that the outliers themselves do not distort the distance. | 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. |
| ScholarGateAdatkészlet ↗ |
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