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| 逐步回归× | 偏最小二乘回归 (PLS)× | |
|---|---|---|
| 领域≠ | 统计学 | 机器学习 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份≠ | 1960 | 1975 |
| 提出者≠ | M. A. Efroymson | Herman Wold; popularized by Svante Wold in chemometrics |
| 类型≠ | Automated variable selection | Supervised latent-variable regression |
| 开创性文献≠ | Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley. link ↗ | Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. DOI ↗ |
| 别名≠ | stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection | PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler |
| 相关≠ | 5 | 3 |
| 摘要≠ | Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library. | Partial least squares regression predicts a response from many, often highly collinear predictors by projecting them onto a small set of latent components — but, unlike principal components regression, it chooses those components to maximize their covariance with the response, not just the variance of the predictors. This supervised dimension reduction makes PLS a workhorse in chemometrics, spectroscopy, and other wide-data settings where predictors vastly outnumber observations. |
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