Comparer des méthodes

	Estimation par noyau de la densité et tests de distribution (KDE)	Test de Lilliefors pour la normalité
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1956	1967
Auteur d'origine	Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman	Hubert W. Lilliefors
Type	Nonparametric density estimation	Goodness-of-fit / normality test
Source fondatrice	Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837.	Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399-402.
Alias	kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation	Lilliefors corrected Kolmogorov-Smirnov test, Lilliefors normality test, Lilliefors Testi
Apparentées	4	5
Résumé	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 Lilliefors test is a goodness-of-fit test that checks whether a continuous sample comes from a normal (or exponential) distribution when the mean and variance are unknown and estimated from the data. Introduced by Hubert W. Lilliefors in 1967, it adjusts the critical values of the Kolmogorov-Smirnov test so that they remain valid once the distribution's parameters are estimated rather than known in advance.