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| Regresión por Mínimos Cuadrados Ordinarios (MCO)× | Estimador de Theil-Sen× | |
|---|---|---|
| Campo≠ | Econometría | Estadística |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2019 | 1968 |
| Autor original≠ | Wooldridge (textbook treatment); classical least squares | Henri Theil (1950); P. K. Sen (1968) |
| Tipo≠ | Linear regression | Robust linear regression |
| Fuente seminal≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗ |
| Alias≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | 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). | The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%. |
| ScholarGateConjunto de datos ↗ |
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