Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresia prin metoda celor mai mici pătrate trunchiate (LTS)× | Regresia prin metoda celor mai mici pătrate ordinare (OLS)× | Estimarea robustă a covarianței (MCD)× | |
|---|---|---|---|
| Domeniu≠ | Statistică | Econometrie | Statistică |
| Familie | Regression model | Regression model | Regression model |
| Anul apariției≠ | 1984 | 2019 | 1999 |
| Autorul original≠ | Peter J. Rousseeuw | Wooldridge (textbook treatment); classical least squares | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) |
| Tip≠ | Robust linear regression | Linear regression | Robust multivariate location-scatter estimator |
| Sursa seminală≠ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223. DOI ↗ |
| Denumiri alternative≠ | LTS, least trimmed squares regression, trimmed least squares, robust regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD) |
| Înrudite≠ | 5 | 5 | 4 |
| Rezumat≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation. |
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