方法对比
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| 有序逻辑回归（比例优势模型）× | 泊松回归与负二项回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010 | 1998 |
| 提出者≠ | Agresti (textbook treatment); proportional odds model | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| 类型≠ | Ordinal logistic regression | Generalized linear model for count data |
| 开创性文献≠ | Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| 别名 | proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds) | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| 相关≠ | 5 | 4 |
| 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. | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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