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| Nelinearni OLS (Nelinearni najmanji kvadrati)× | Regresija običnih najmanjih kvadrata (OLS)× | |
|---|---|---|
| Područje | Ekonometrija | Ekonometrija |
| Obitelj | Regression model | Regression model |
| Godina nastanka≠ | 1974–1987 | 2019 |
| Tvorac≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Wooldridge (textbook treatment); classical least squares |
| Vrsta≠ | Nonlinear regression estimator | Linear regression |
| Temeljni izvor≠ | Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Drugi nazivi | nonlinear least squares, NLS, NLLS, nonlinear regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Srodne | 5 | 5 |
| Sažetak≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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