方法对比
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| 有序逻辑回归× | 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≠ | 1980 | 2019 |
| 提出者≠ | Peter McCullagh | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Ordinal regression / GLM | Linear regression |
| 开创性文献≠ | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | proportional-odds model, cumulative link model, ordered logit, OLR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 6 | 5 |
| 摘要≠ | Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds. | 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). |
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