পদ্ধতির তুলনা করুন

	অরৈখিক ওএলএস (অরৈখিক ক্ষুদ্রতম বর্গ)	সাধারণীকৃত ন্যূনতম বর্গ (GLS)
ক্ষেত্র	অর্থমিতি	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1974–1987	1935
প্রবর্তক	Gallant (1987); Wooldridge (2010) for econometric treatment	Alexander Craig Aitken
ধরন	Nonlinear regression estimator	Linear estimator
মৌলিক উৎস	Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600	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	nonlinear least squares, NLS, NLLS, nonlinear regression	GLS, Aitken estimator, EGLS, feasible GLS
সম্পর্কিত	5	3
সারসংক্ষেপ	Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal.	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.