Compară metode

	Interpolație spațială Kriging	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Analiză spațială	Econometrie
Familie	Regression model	Regression model
Anul apariției	1963	2019
Autorul original	Georges Matheron (formalised geostatistics)	Wooldridge (textbook treatment); classical least squares
Tip	Geostatistical spatial interpolation	Linear regression
Sursa seminală	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
Denumiri alternative	geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Înrudite	5	5
Rezumat	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).