Methoden vergleichen

	Least Trimmed Squares (LTS)-Regression	Methode der kleinsten Quadrate (OLS)
Fachgebiet	Statistik	Ökonometrie
Familie	Regression model	Regression model
Entstehungsjahr	1984	2019
Urheber	Peter J. Rousseeuw	Wooldridge (textbook treatment); classical least squares
Typ	Robust linear regression	Linear regression
Wegweisende Quelle	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Aliasnamen	LTS, least trimmed squares regression, trimmed least squares, robust regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Verwandt	5	5
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.	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).