Comparer des méthodes

	Interpolation spatiale par krigeage	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Analyse spatiale	Économétrie
Famille	Regression model	Regression model
Année d'origine	1963	2019
Auteur d'origine	Georges Matheron (formalised geostatistics)	Wooldridge (textbook treatment); classical least squares
Type	Geostatistical spatial interpolation	Linear regression
Source fondatrice	Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266.	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
Apparentées	5	5
Résumé	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).