Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel Ordinary Kriging× | Parastā krigēšana× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1963 (Ordinary Kriging origin); panel extensions formalized in 1990s–2000s | 1963 |
| Autors≠ | Extension of Ordinary Kriging (Matheron, 1963) to panel/longitudinal spatial settings | Georges Matheron (formalising D.G. Krige's empirical work) |
| Tips≠ | Geostatistical spatial interpolation | Geostatistical interpolation |
| Pirmavots≠ | Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience. ISBN: 978-0471002550 | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Citi nosaukumi | ordinary kriging for panel data, longitudinal ordinary kriging, repeated-measures spatial kriging, panel geostatistical interpolation | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Panel Ordinary Kriging extends the classical geostatistical interpolation method — Ordinary Kriging — to panel (longitudinal) datasets where the same set of spatial locations is observed repeatedly over multiple time periods. It produces optimal linear unbiased predictions at unsampled locations for each time slice, accounting for spatial dependence while leveraging the temporal structure of the repeated observations. | Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point. |
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