Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Нелинейна ОНЛ (Нелинейни най-малки квадрати)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1974–1987 | 2019 |
| Създател≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Nonlinear regression estimator | Linear regression |
| Основополагащ източник≠ | 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 |
| Други названия | nonlinear least squares, NLS, NLLS, nonlinear regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 |
| Резюме≠ | 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). |
| ScholarGateНабор от данни ↗ |
|
|