השוואת שיטות

	אמידת צפיפות גרעין ובדיקת התפלגות (KDE)	מבחן אנדרסון-דארלינג לנורמליות
תחום	סטטיסטיקה	סטטיסטיקה
משפחה	Regression model	Regression model
שנת המקור	1956	1952
הוגה השיטה	Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman	Anderson & Darling (1952); EDF tables by Stephens (1974)
סוג	Nonparametric density estimation	Empirical distribution function (EDF) goodness-of-fit test
מקור מכונן	Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837.	Anderson, T. W., & Darling, D. A. (1952). Asymptotic Theory of Certain 'Goodness of Fit' Criteria Based on Stochastic Processes. The Annals of Mathematical Statistics, 23(2), 193-212.
כינויים	kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation	Anderson-Darling Normallik Testi, A-squared test, AD test, Anderson-Darling goodness-of-fit test
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.	The Anderson-Darling test is an empirical distribution function (EDF) goodness-of-fit test, introduced by Anderson and Darling in 1952, that checks whether a continuous sample comes from a specified distribution such as the normal, exponential, or Weibull. By weighting deviations more heavily in the tails, it detects departures in the distribution's extremes more powerfully than the Kolmogorov-Smirnov test.