পদ্ধতির তুলনা করুন

	Robust Weighted Least Squares (Robust WLS)	Weighted Least Squares (WLS)
ক্ষেত্র	অর্থমিতি	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1964/1981	1935
প্রবর্তক	Huber, P. J.	Alexander Craig Aitken
ধরন	Robust weighted regression	Weighted linear estimator
মৌলিক উৎস	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
অপর নাম	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
সম্পর্কিত	5	3
সারসংক্ষেপ	Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.