方法对比
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| 有序逻辑回归（比例优势模型）× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 统计学 | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2010 | 1958 |
| 提出者≠ | Agresti (textbook treatment); proportional odds model | David Roxbee Cox |
| 类型≠ | Ordinal logistic regression | Method |
| 开创性文献≠ | Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 别名≠ | proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds) | logit model, binomial logistic regression, LR |
| 相关≠ | 5 | 3 |
| 摘要≠ | Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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