Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel Ordinary Kriging× | Telpiskās regresijas paneļa analīze× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1963 (Ordinary Kriging origin); panel extensions formalized in 1990s–2000s | 1988-2014 |
| Autors≠ | Extension of Ordinary Kriging (Matheron, 1963) to panel/longitudinal spatial settings | Anselin, Elhorst, and colleagues in spatial econometrics |
| Tips≠ | Geostatistical spatial interpolation | Spatial panel regression |
| Pirmavots≠ | Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience. ISBN: 978-0471002550 | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 |
| Citi nosaukumi | ordinary kriging for panel data, longitudinal ordinary kriging, repeated-measures spatial kriging, panel geostatistical interpolation | spatial panel model, panel spatial econometrics, spatial panel data regression, PSR |
| Saistītās | 6 | 6 |
| 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. | Panel Spatial Regression extends standard panel data models by explicitly accounting for spatial dependence among cross-sectional units observed over time. It combines the temporal control of panel fixed or random effects with a spatial weights matrix that encodes geographic or network proximity, yielding unbiased and efficient estimates when observations are spatially correlated across units. |
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