Comparar métodos

	Estimativa de Densidade por Kernel em Painel	Estimação Local de Densidade por Kernel
Área	Análise espacial	Análise espacial
Família	Regression model	Regression model
Ano de origem	1962 (KDE); panel extension: 1990s–2000s	1985-1986
Autor original	Parzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literature	Silverman, B. W.; Diggle, P. J.
Tipo	Nonparametric density estimation	Non-parametric density estimator
Fonte seminal	Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065-1076.	Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. ISBN: 978-0412246203
Outros nomes	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
Relacionados	5	5
Resumo	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.