পদ্ধতির তুলনা করুন

	Least Trimmed Squares (LTS) Regression	এম-Estimator (Robust Regression)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1984	2009
প্রবর্তক	Peter J. Rousseeuw	Peter J. Huber
ধরন	Robust linear regression	Robust linear regression
মৌলিক উৎস	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.
অপর নাম	LTS, least trimmed squares regression, trimmed least squares, robust regression	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler
সম্পর্কিত	5	5
সারসংক্ষেপ	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.	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.