Regression model
核密度估计与分布检验 (KDE)
核密度估计是一种非参数方法，它通过在每个观测值上放置一个光滑的核函数来估计连续概率密度，而不假设任何参数分布。它起源于 Rosenblatt (1956) 的研究以及 Silverman (1986) 的教科书式处理，并且它还支持基于估计密度构建的分布比较检验。
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来源
- 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
如何引用本页
ScholarGate. (2026, June 1). Kernel Density Estimation and Distribution Testing (KDE). ScholarGate. https://scholargate.app/zh/statistics/kernel-density-test
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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