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تقدير كثافة النواة للبيانات المقطعية×تحليل الانحدار المكاني للبيانات المقطعية (Panel Spatial Regression)×
المجالالتحليل المكانيالتحليل المكاني
العائلةRegression modelRegression model
سنة النشأة1962 (KDE); panel extension: 1990s–2000s1988-2014
صاحب الطريقةParzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literatureAnselin, Elhorst, and colleagues in spatial econometrics
النوعNonparametric density estimationSpatial panel regression
المصدر التأسيسيParzen, 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
الأسماء البديلةPanel KDE, longitudinal kernel density estimation, repeated-measures KDE, panel nonparametric density estimationspatial panel model, panel spatial econometrics, spatial panel data regression, PSR
ذات صلة56
الملخصPanel 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.
ScholarGateمجموعة البيانات
  1. v1
  2. 2 المصادر
  3. PUBLISHED
  1. v1
  2. 2 المصادر
  3. PUBLISHED

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ScholarGateقارن الطرق: Panel Kernel Density Estimation · Panel Spatial Regression. استُرجع بتاريخ 2026-06-15 من https://scholargate.app/ar/compare