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تقدير كثافة النواة للبيانات المقطعية×تقدير كثافة النواة المحلي×
المجالالتحليل المكانيالتحليل المكاني
العائلةRegression modelRegression model
سنة النشأة1962 (KDE); panel extension: 1990s–2000s1985-1986
صاحب الطريقةParzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literatureSilverman, B. W.; Diggle, P. J.
النوعNonparametric density estimationNon-parametric density estimator
المصدر التأسيسيParzen, 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
الأسماء البديلةPanel KDE, longitudinal kernel density estimation, repeated-measures KDE, panel nonparametric density estimationLocal KDE, adaptive KDE, spatially adaptive kernel density estimation, local density estimation
ذات صلة55
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.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.
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 · Local Kernel Density Estimation. استُرجع بتاريخ 2026-06-15 من https://scholargate.app/ar/compare