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| 逻辑回归× | 泊松回归与负二项回归× | |
|---|---|---|
| 领域≠ | 研究统计学 | 计量经济学 |
| 方法族≠ | Process / pipeline | Regression model |
| 起源年份≠ | 1958 | 1998 |
| 提出者≠ | David Roxbee Cox | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| 类型≠ | Method | Generalized linear model for count data |
| 开创性文献≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| 别名≠ | logit model, binomial logistic regression, LR | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| 相关≠ | 3 | 4 |
| 摘要≠ | 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. | 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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