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| Regression with Ordinary Least Squares (OLS)× | Stima Robusta della Covarianza (MCD)× | |
|---|---|---|
| Campo≠ | Econometria | Statistica |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2019 | 1999 |
| Ideatore≠ | Wooldridge (textbook treatment); classical least squares | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) |
| Tipo≠ | Linear regression | Robust multivariate location-scatter estimator |
| Fonte seminale≠ | 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 ↗ |
| Alias | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD) |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | 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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