方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 克里金空间插值× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 空间分析 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1963 | 2019 |
| 提出者≠ | Georges Matheron (formalised geostatistics) | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Geostatistical spatial interpolation | Linear regression |
| 开创性文献≠ | 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 |
| 别名 | geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关 | 5 | 5 |
| 摘要≠ | 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). |
| ScholarGate数据集 ↗ |
|
|