Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Mínimos Cuadrados Generalizados (GLS)× | Regresión por Mínimos Cuadrados Ordinarios (MCO)× | |
|---|---|---|
| Campo≠ | Estadística | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1935 | 2019 |
| Autor original≠ | Alexander Craig Aitken | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Linear estimator | Linear regression |
| Fuente seminal≠ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | GLS, Aitken estimator, EGLS, feasible GLS | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados≠ | 3 | 5 |
| Resumen≠ | 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. | 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). |
| ScholarGateConjunto de datos ↗ |
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