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| Stima di Densità Kernel e Test di Distribuzione (KDE)× | Regressione quantilica× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1956 | 1978 |
| Ideatore≠ | Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman | Koenker & Bassett |
| Tipo≠ | Nonparametric density estimation | Conditional quantile regression |
| Fonte seminale≠ | Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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