Methoden vergleichen

	Least Trimmed Squares (LTS)-Regression	Robuste Kovarianzschätzung (MCD)
Fachgebiet	Statistik	Statistik
Familie	Regression model	Regression model
Entstehungsjahr	1984	1999
Urheber	Peter J. Rousseeuw	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Typ	Robust linear regression	Robust multivariate location-scatter estimator
Wegweisende Quelle	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Aliasnamen	LTS, least trimmed squares regression, trimmed least squares, robust regression	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Verwandt	5	4
Zusammenfassung	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.	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.