方法对比
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| 泊松回归与负二项回归× | 逻辑回归× | 面板数据固定效应模型× | |
|---|---|---|---|
| 领域≠ | 计量经济学 | 研究统计学 | 计量经济学 |
| 方法族≠ | Regression model | Process / pipeline | Regression model |
| 起源年份≠ | 1998 | 1958 | 2014 |
| 提出者≠ | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) | David Roxbee Cox | Hsiao (textbook treatment); within transformation of panel data |
| 类型≠ | Generalized linear model for count data | Method | Panel data regression |
| 开创性文献≠ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| 别名≠ | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon | logit model, binomial logistic regression, LR | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| 相关≠ | 4 | 3 | 5 |
| 摘要≠ | 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. | 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. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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