方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| LOESS / LOWESS局部回归× | 多项式回归× | |
|---|---|---|
| 领域≠ | 机器学习 | 统计学 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 1979 | 2012 |
| 提出者≠ | William S. Cleveland | Montgomery, Peck & Vining (textbook treatment); classical least squares |
| 类型≠ | Local nonparametric regression smoother | Linear regression in transformed predictors |
| 开创性文献≠ | Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI ↗ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 |
| 别名≠ | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon | polynomial least squares, curvilinear regression, Polinom Regresyonu |
| 相关≠ | 3 | 4 |
| 摘要≠ | LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots. | Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends. |
| ScholarGate数据集 ↗ |
|
|