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Panel Kernel Density Estimation×Панелна пространствена регресия×
ОбластПространствен анализПространствен анализ
СемействоRegression modelRegression model
Година на възникване1962 (KDE); panel extension: 1990s–2000s1988-2014
СъздателParzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literatureAnselin, Elhorst, and colleagues in spatial econometrics
ТипNonparametric density estimationSpatial panel regression
Основополагащ източникParzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065-1076. DOI ↗Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408
Други названияPanel KDE, longitudinal kernel density estimation, repeated-measures KDE, panel nonparametric density estimationspatial panel model, panel spatial econometrics, spatial panel data regression, PSR
Свързани56
РезюмеPanel 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 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.
ScholarGateНабор от данни
  1. v1
  2. 2 Източници
  3. PUBLISHED
  1. v1
  2. 2 Източници
  3. PUBLISHED

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ScholarGateСравнение на методи: Panel Kernel Density Estimation · Panel Spatial Regression. Извлечено на 2026-06-15 от https://scholargate.app/bg/compare