Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Estimación por Desviación Absoluta Mediana (MAD)× | Regresión por Mínimos Cuadrados Ordinarios (MCO)× | |
|---|---|---|
| Campo≠ | Estadística | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1974 | 2019 |
| Autor original≠ | Hampel (influence-curve treatment); classical robust statistics | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Robust scale estimator | Linear regression |
| Fuente seminal≠ | Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados | 5 | 5 |
| Resumen≠ | Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result. | 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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