So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Khoảng cách Mahalanobis Mạnh mẽ× | Hồi quy Bình phương Nhỏ nhất Cắt tỉa (Least Trimmed Squares - LTS)× | |
|---|---|---|
| Lĩnh vực | Thống kê | Thống kê |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 1990 | 1984 |
| Người khởi xướng≠ | Rousseeuw & Van Zomeren (robust distance); Filzmoser, Garrett & Reimann (multivariate outlier detection) | Peter J. Rousseeuw |
| Loại≠ | Robust multivariate outlier detection | Robust linear regression |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác≠ | MCD Mahalanobis distance, robust mahalanobis, minimum covariance determinant distance, Robust Mahalanobis Uzaklığı | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Liên quan | 5 | 5 |
| Tóm tắt≠ | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|