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	Least Trimmed Squares (LTS) Regression	Vanligaste minsta kvadratmetoden (OLS) Regression
Ämnesområde	Statistik	Ekonometri
Familj	Regression model	Regression model
Ursprungsår	1984	2019
Upphovsperson	Peter J. Rousseeuw	Wooldridge (textbook treatment); classical least squares
Typ	Robust linear regression	Linear regression
Ursprungskälla	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
Alias	LTS, least trimmed squares regression, trimmed least squares, robust regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Närliggande	5	5
Sammanfattning	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).