Machine learning
LOESS / LOWESS局部回归
LOESS（局部估计散点平滑）由William Cleveland于1979年引入，并于1988年与Susan Devlin一起扩展，通过在每个点邻域内执行单独的加权多项式回归来拟合平滑曲线。附近观测值比远处观测值更重要，因此该方法遵循局部结构，而不假设任何全局函数形式，使其成为散点图流行的探索性平滑器。
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来源
- Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI: 10.1080/01621459.1979.10481038 ↗
- Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610. DOI: 10.1080/01621459.1988.10478639 ↗
如何引用本页
ScholarGate. (2026, June 2). Local Regression (LOESS / LOWESS). ScholarGate. https://scholargate.app/zh/machine-learning/loess
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