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。
| 广义线性模型 (GLM)× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1972 | 2019 |
| 提出者≠ | John A. Nelder & Robert W. M. Wedderburn | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Regression framework | Linear regression |
| 开创性文献≠ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | GLM, generalized regression, exponential family regression, link-function model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 6 | 5 |
| 摘要≠ | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGate数据集 ↗ |
|
|