Jämför metoder

	Least Trimmed Squares (LTS) Regression	Vanligaste minsta kvadratmetoden (OLS) Regression	Kvantilregression	Robust kovariansestimering (MCD)
Ämnesområde	Statistik	Ekonometri	Ekonometri	Statistik
Familj	Regression model	Regression model	Regression model	Regression model
Ursprungsår	1984	2019	1978	1999
Upphovsperson	Peter J. Rousseeuw	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Typ	Robust linear regression	Linear regression	Conditional quantile regression	Robust multivariate location-scatter estimator
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	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Alias	LTS, least trimmed squares regression, trimmed least squares, robust regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Närliggande	5	5	5	4
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).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.