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Оцена густине језгра и тестирање расподеле (KDE)×Kvantilna regresija×
OblastStatistikaEkonometrija
PorodicaRegression modelRegression model
Godina nastanka19561978
TvoracRosenblatt (1956); Parzen (1962); textbook treatment by SilvermanKoenker & Bassett
TipNonparametric density estimationConditional quantile regression
Temeljni izvorRosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Drugi nazivikernel density estimate, KDE, Parzen window estimation, nonparametric density estimationconditional quantile regression, regression quantiles, Kantil Regresyon
Srodne45
SažetakKernel Density Estimation is a nonparametric method that estimates a continuous probability density by placing a smooth kernel function over each observation, without assuming any parametric distribution. It traces back to Rosenblatt (1956) and the textbook treatment by Silverman (1986), and it also supports distribution-comparison tests built on the estimated densities.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
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ScholarGateUporedite metode: Kernel Density Estimation · Quantile Regression. Preuzeto 2026-06-15 sa https://scholargate.app/sr/compare