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| 有序逻辑回归× | 广义线性模型 (GLM)× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1980 | 1972 |
| 提出者≠ | Peter McCullagh | John A. Nelder & Robert W. M. Wedderburn |
| 类型≠ | Ordinal regression / GLM | Regression framework |
| 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 ↗ | 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 ↗ |
| 别名 | proportional-odds model, cumulative link model, ordered logit, OLR | GLM, generalized regression, exponential family regression, link-function model |
| 相关 | 6 | 6 |
| 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. | 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. |
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