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| クリギング空間補間× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 空間分析 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1963 | 2019 |
| 提唱者≠ | Georges Matheron (formalised geostatistics) | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Geostatistical spatial interpolation | Linear regression |
| 原典≠ | 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 |
| 別名 | geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連 | 5 | 5 |
| 概要≠ | 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). |
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