Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Estimation par noyau de la densité et tests de distribution (KDE)× | Test du médian de Mood× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1956 | 1954 |
| Auteur d'origine≠ | Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman | A. M. Mood |
| Type≠ | Nonparametric density estimation | Nonparametric median comparison |
| Source fondatrice≠ | Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. DOI ↗ | Mood, A. M. (1954). On the Asymptotic Efficiency of Certain Nonparametric Two-Sample Tests. Annals of Mathematical Statistics, 25(3), 514-522. DOI ↗ |
| Alias≠ | kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation | median test, Brown-Mood median test, Mood Medyan Testi |
| Apparentées≠ | 4 | 3 |
| 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. | Mood's median test is a nonparametric procedure that compares the medians of k independent groups by counting how many observations in each group fall above and below the pooled (grand) median, then applying a chi-square test to the resulting 2×k contingency table. It traces to A. M. Mood's 1954 work on nonparametric two-sample tests. |
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