Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diagnóstico de influencia (distancia de Cook, DFFITS, apalancamiento)× | Regresión por Mínimos Cuadrados Ordinarios (MCO)× | |
|---|---|---|
| Campo≠ | Estadística | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1977 | 2019 |
| Autor original≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Regression diagnostic | Linear regression |
| Fuente seminal≠ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | Cook's distance, DFFITS, leverage, influential observation detection | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados | 5 | 5 |
| Resumen≠ | Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients. | 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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