Usporedite metode

Pregledajte odabrane metode jednu uz drugu; retci koji se razlikuju su istaknuti.

Procjena gustoće kernelom i testiranje distribucije (KDE)×Kvantilna regresija×
PodručjeStatistikaEkonometrija
ObiteljRegression modelRegression model
Godina nastanka19561978
TvoracRosenblatt (1956); Parzen (1962); textbook treatment by SilvermanKoenker & Bassett
VrstaNonparametric 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.
ScholarGateSkup podataka
  1. v1
  2. 2 Izvori
  3. PUBLISHED
  1. v1
  2. 2 Izvori
  3. PUBLISHED

Idi na pretraživanje Download slides

ScholarGateUsporedite metode: Kernel Density Estimation · Quantile Regression. Preuzeto 2026-06-15 s https://scholargate.app/hr/compare