方法证据记录
Kernel Density Estimation
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Kernel Density Estimation and Distribution Testing (KDE)
分类方法记录 · regression-model / statistics
- Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. · DOI 10.1214/aoms/1177728190
- Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall / CRC Press. · ISBN 978-0412246203
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