השוואת שיטות

	אמידת צפיפות גרעין ובדיקת התפלגות (KDE)	רגרסיית קוונטילים
תחום	סטטיסטיקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1956	1978
הוגה השיטה	Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman	Koenker & Bassett
סוג	Nonparametric density estimation	Conditional quantile regression
מקור מכונן	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.
כינויים	kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation	conditional quantile regression, regression quantiles, Kantil Regresyon
קשורות	4	5
תקציר	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.