পদ্ধতির তুলনা করুন

	ননলিনিয়ার ওয়েটেড লিস্ট স্কোয়ার্স (NWLS)	সাধারণীকৃত ন্যূনতম বর্গ (GLS)	Weighted Least Squares (WLS)
ক্ষেত্র	অর্থমিতি	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1960s–1980s (formalized in applied econometrics)	1935	1935
প্রবর্তক	Extension of Gauss-Newton nonlinear least squares with Aitken-type weighting	Alexander Craig Aitken	Alexander Craig Aitken
ধরন	Nonlinear regression estimator	Linear estimator	Weighted linear estimator
মৌলিক উৎস	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson Education. ISBN: 978-0134461366	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	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	NWLS, nonlinear weighted least squares, weighted nonlinear regression, heteroscedasticity-corrected nonlinear regression	GLS, Aitken estimator, EGLS, feasible GLS	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
সম্পর্কিত	3	3	3
সারসংক্ষেপ	Nonlinear Weighted Least Squares combines the flexibility of nonlinear regression with the variance-stabilizing power of observation-level weights. It minimises a weighted sum of squared residuals around a user-specified nonlinear mean function, making it the method of choice when the relationship is inherently nonlinear and error variance differs across observations.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.	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.