方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 负二项回归× | 普通最小二乘法 (OLS) 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≠ | 2011 | 2019 |
| 提出者≠ | Hilbe (textbook treatment); generalized linear model framework | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Generalized linear model for count data | Linear regression |
| 开创性文献≠ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名≠ | NB regression, NB2 regression, negatif binom regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGate数据集 ↗ |
|
|