Salīdzināt metodes

	Kodola blīvuma novērtēšana un sadalījuma testēšana (KDE)	Kvantīļu regresija
Nozare	Statistika	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1956	1978
Autors	Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman	Koenker & Bassett
Tips	Nonparametric density estimation	Conditional quantile regression
Pirmavots	Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Citi nosaukumi	kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation	conditional quantile regression, regression quantiles, Kantil Regresyon
Saistītās	4	5
Kopsavilkums	Kernel 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.