Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Interpolación Espacial por Kriging× | Regresión por Mínimos Cuadrados Ordinarios (MCO)× | |
|---|---|---|
| Campo≠ | Análisis espacial | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1963 | 2019 |
| Autor original≠ | Georges Matheron (formalised geostatistics) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Geostatistical spatial interpolation | Linear regression |
| Fuente seminal≠ | Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados | 5 | 5 |
| Resumen≠ | Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms. | 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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