Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel Kernel Density Estimation× | Lokālā kodola blīvuma novērtēšana× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1962 (KDE); panel extension: 1990s–2000s | 1985-1986 |
| Autors≠ | Parzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literature | Silverman, B. W.; Diggle, P. J. |
| Tips≠ | Nonparametric density estimation | Non-parametric density estimator |
| Pirmavots≠ | Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065-1076. DOI ↗ | Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203 |
| Citi nosaukumi | Panel KDE, longitudinal kernel density estimation, repeated-measures KDE, panel nonparametric density estimation | Local KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Panel Kernel Density Estimation (Panel KDE) extends the standard kernel density estimator to panel (longitudinal) data, estimating smooth density surfaces for spatial or attribute variables observed across multiple units and time periods. It reveals how the distribution of a phenomenon shifts, concentrates, or disperses over time and across groups, making it a natural tool for tracking spatial patterns in repeated-measures or panel datasets. | Local Kernel Density Estimation (Local KDE) is a non-parametric spatial method that estimates the density of point events at each location by applying a kernel function with a spatially adaptive bandwidth. Unlike global KDE, which uses a fixed bandwidth across the entire study area, Local KDE adjusts the smoothing window according to local data density, capturing fine-scale clustering where events are sparse or concentrated. |
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