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Porovnat metody

Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.

Odhad hustoty jádra pro panelová data×Prostorová autokorelace v panelových datech×
OborProstorová analýzaProstorová analýza
RodinaRegression modelRegression model
Rok vzniku1962 (KDE); panel extension: 1990s–2000s1988–2003
TvůrceParzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literatureAnselin, L.; Elhorst, J. P.
TypNonparametric density estimationDiagnostic test / exploratory statistic
Původní zdrojParzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065-1076. DOI ↗Anselin, L. (2013). Spatial Econometrics: Methods and Models. Springer Netherlands. (Originally published 1988.) ISBN: 978-9401577991
Další názvyPanel KDE, longitudinal kernel density estimation, repeated-measures KDE, panel nonparametric density estimationspatial autocorrelation in panel data, panel spatial dependence, spatio-temporal autocorrelation, cross-sectional dependence in panels
Příbuzné55
ShrnutíPanel 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.Panel Spatial Autocorrelation measures whether observations that are geographically close also tend to have similar values across repeated time periods. It extends classic cross-sectional spatial autocorrelation statistics such as Moran's I to panel data, enabling researchers to detect spatial dependence consistently over time and to diagnose whether a panel regression model requires a spatial component.
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ScholarGatePorovnat metody: Panel Kernel Density Estimation · Panel Spatial Autocorrelation. Získáno 2026-06-15 z https://scholargate.app/cs/compare