Salīdzināt metodes

	Mazākās apgrieztās kvadrātiskās kļūdas (LTS) regresija	Robustā ANOVA (Velča un apgrieztais vidējais)
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	1984	1951
Autors	Peter J. Rousseeuw	Welch (1951); robust trimmed-mean approach popularised by Wilcox
Tips	Robust linear regression	Robust one-way analysis of variance
Pirmavots	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336.
Citi nosaukumi	LTS, least trimmed squares regression, trimmed least squares, robust regression	Welch ANOVA, trimmed-mean ANOVA, heteroscedastic one-way ANOVA, Robust ANOVA (Welch & Trimmed Mean)
Saistītās	5	5
Kopsavilkums	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 ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads.