方法对比
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| 有序逻辑回归（有序 Logit/Probit）× | 负二项回归× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1980 | 2011 |
| 提出者≠ | McCullagh (proportional odds / cumulative model) | Hilbe (textbook treatment); generalized linear model framework |
| 类型≠ | Cumulative ordinal regression | Generalized linear model for count data |
| 开创性文献≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| 别名≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | NB regression, NB2 regression, negatif binom regresyonu |
| 相关 | 4 | 4 |
| 摘要≠ | Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. |
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