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| Estimació de Densitat Kernel i Proves de Distribució (KDE)× | Test d'Anderson-Darling de normalitat× | |
|---|---|---|
| Camp | Estadística | Estadística |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1956 | 1952 |
| Autor original≠ | Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman | Anderson & Darling (1952); EDF tables by Stephens (1974) |
| Tipus≠ | Nonparametric density estimation | Empirical distribution function (EDF) goodness-of-fit test |
| Font seminal≠ | Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. DOI ↗ | 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. DOI ↗ |
| Àlies≠ | 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 |
| Relacionats≠ | 4 | 5 |
| Resum≠ | 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. |
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