Porovnať metódy

	Odhad hustoty pomocou jadrových funkcií a testovanie rozdelenia (KDE)	Lilliefortsov test normality
Odbor	Štatistika	Štatistika
Rodina	Regression model	Regression model
Rok vzniku	1956	1967
Tvorca	Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman	Hubert W. Lilliefors
Typ	Nonparametric density estimation	Goodness-of-fit / normality test
Pôvodný zdroj	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.
Ďalšie názvy	kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation	Lilliefors corrected Kolmogorov-Smirnov test, Lilliefors normality test, Lilliefors Testi
Príbuzné	4	5
Zhrnutie	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.