পদ্ধতির তুলনা করুন

	Robust OLS (OLS with Robust Standard Errors)	Weighted Least Squares (WLS)
ক্ষেত্র	অর্থমিতি	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1980	1935
প্রবর্তক	Halbert White	Alexander Craig Aitken
ধরন	Linear regression with robust inference	Weighted linear estimator
মৌলিক উৎস	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	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	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
সম্পর্কিত	6	3
সারসংক্ষেপ	Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.	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.